Banking system controlled responsive to data bearing records

ABSTRACT

An automated banking machine operates to carry out financial transfers responsive to data read form data bearing records. The machine includes a card reader that operates to read data from user cards. The card data corresponds to financial accounts. The automated banking machine further includes a cash dispenser and a receipt printer. The machine is operative to dispense cash to an authorized user based on identifying data including card data, and to cause a financial transfer from an account corresponding to card data through communication with at least one remote computer. The machine includes a check acceptor that operates to determine magnetic symbols included on checks. Cash may be dispensed in exchange for a check received through operation of the check acceptor of the machine.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit under 35 U.S.C. §119(e) of ProvisionalApplication Ser. Nos. 61/065,378; 61/065,334; 61/065,337; 61/065,304;61/065,302; 61/065,303; 61/065,338; 61/065,331; and 61/065,336 each ofwhich was filed Feb. 11, 2008.

This application is also a continuation-in-part of U.S. application Ser.No. 11/371,372 filed Mar. 8, 2006 which application claims the benefitpursuant to 35 U.S.C. §119(e) of Provisional Application Ser. Nos.60/660,075 and 60/659,990 each filed Mar. 9, 2005. U.S. application Ser.No. 11/371,372 is also a continuation-in-part of U.S. application Ser.No. 09/723,304 filed Nov. 27, 2000, which claims benefit pursuant to 35U.S.C. §119(e) of Provisional Application Ser. No. 60/167,996 filed Nov.30, 1999.

The disclosures of each of these applications is incorporated herein byreference in their entirety.

TECHNICAL FIELD

This invention relates to automated banking machines that operateresponsive to data read from user cards and which may be classified inU.S. Class 235, Subclass 379.

BACKGROUND ART

Automated banking machines may include a card reader that operates toread data from a bearer record such as a user card. The automatedbanking machine may operate to cause the data read from the card to becompared with other computer stored data related to the bearer. Themachine operates in response to the comparison determining that thebearer is an authorized system user to carry out at least onetransaction which is operative to transfer value to or from at least oneaccount. A record of the transaction is also commonly printed throughoperation of the automated banking machine and provided to the user. Acommon type of automated banking machine used by consumers is anautomated teller machine which enables customers to carry out bankingtransactions. Banking transactions carried out may include thedispensing of cash, the making of deposits, the transfer of fundsbetween accounts and account balance inquiries. The types of bankingtransactions a customer can carry out are determined by the capabilitiesof the particular banking machine and the programming of the institutionoperating the machine.

Other types of automated banking machines may be operated by merchantsto carry out commercial transactions. These transactions may include,for example, the acceptance of deposit bags, the receipt of checks orother financial instruments, the dispensing of rolled coin or othertransactions required by merchants. Still other types of automatedbanking machines may be used by service providers in a transactionenvironment such as at a bank to carry out financial transactions. Suchtransactions may include for example, the counting and storage ofcurrency notes or other financial instrument sheets, the dispensing ofnotes or other sheets, the imaging of checks or other financialinstruments, and other types of service provider transactions. Forpurposes of this disclosure an automated banking machine or an ATM shallbe deemed to include any machine that may be used to electronicallycarry out transactions involving transfers of value.

Automated banking machines may benefit from improvements.

OBJECTS OF EXEMPLARY EMBODIMENTS

It is an object of an example embodiment to provide an automated bankingmachine that is operative responsive to data included on user cards.

It is a further object of an example embodiment to provide an automatedbanking machine system and method that accepts deposits and providescash to a user.

It is a further object of an example embodiment to provide a depositaccepting apparatus.

It is a further object of an example embodiment to provide a depositaccepting apparatus for use in connection with an automated bankingmachine.

It is a further object of an example embodiment to provide a depositaccepting apparatus which can be used to accept, image and verify theauthenticity of items.

It is a further object of an example embodiment to provide a depositaccepting apparatus that can be used in existing automated bankingmachine systems.

It is a further object of an example embodiment to provide a depositaccepting apparatus that has greater reliability.

It is a further object of an example embodiment to provide a depositaccepting apparatus and method that can accurately detect MICR symbolspeaks and peak intervals.

It is a further object of an example embodiment to provide a depositaccepting apparatus and method that is operative to read MICR symbols inany four check orientations.

It is a further object of an example embodiment to provide a method thatis operative to accurately place magnetic symbol peaks after a glitch intransport frequency.

It is a further object of an example embodiment to provide a method thatis operative to recognize MICR symbols on checks by correlating magneticwaveform peaks to feature vectors representing MICR symbol peaks.

It is a further object of an example embodiment to provide a method thatis operative to recognize MICR symbols on checks by correlating magneticwaveform peak distances to feature vectors representing MICR symbolpeaks distances.

It is a further object of an example embodiment to provide a method thatis operative to recognize MICR symbols on checks by flagging magneticwaveform peaks that appear redundant, out of place or missing andprojecting where they should be located on the magnetic waveform.

It is a further object of an example embodiment to provide a method thatis operative to optically locate MICR symbols using a limited set ofoptical scan lines.

It is a further object of an example embodiment to provide a method thatis operative to more accurately filter signals corresponding to magneticwaveforms to allow better recovery of magnetic waveform peaks.

It is a further object of an example embodiment to provide a method thatis operative to better analyze possible magnetic waveform peaks todetermine valid waveform peaks.

It is a further object of an example embodiment to provide a depositaccepting apparatus and method that is operative to use both magneticand optical data to accurately recognize MICR symbols.

It is a further object of an example embodiment to provide methods ofaccepting deposited items.

It is a further object of an example embodiment to provide methods ofoptically scanning a MICR symbol and then correlating the optical scanwith a predetermined feature vector.

It is a further object of an example embodiment to provide a method forverifying the authenticity of deposited items.

It is a further object of an example embodiment to provide a method forverifying the authenticity of a deposited check.

It is a further object of an example embodiment to provide a method forhandling and storing deposited items.

It is a further object of an example embodiment to provide an apparatusand method for correlating image and transaction data to facilitatecheck processing.

Further objects of example embodiments will be made apparent in thefollowing Detailed Description of Example Embodiments and the appendedclaims.

The foregoing objects are accomplished in example embodiments by adeposit accepting apparatus and method used in connection with anautomated banking machine. The machine includes a housing with a depositaccepting apparatus therein. One example deposit accepting apparatusincludes a transport section. The transport section includes a transportwhich accepts items of variable thickness. The transport sectionincludes a biasing mechanism for reliably engaging deposited items withmoving mechanisms such as belts or rollers in the transport section. Thedeposited items are reliably engaged with such moving members to assurethat the deposited item is moved through the transport section.

The example transport section further includes an analysis moduleadjacent thereto. In the example embodiment the analysis module servesas an imaging device and is operative to analyze documents passingthrough the transport section. For purposes of this application animaging device includes any device that is operative to enable thegeneration of image data which corresponds to a visual image of at leasta portion of the document. In addition the analysis module is operativeto sense for features and characteristics of the document which may beused to identify the document type. Alternatively or in addition theanalysis module may operate to sense properties of a deposited documentwhich distinguish acceptable or genuine documents from unacceptabledocuments.

An example embodiment may include an apparatus comprising at least onemagnetic sensor, at least one transport, a data store, and at least oneprocessor. The at least one transport may be operative to move a checkacross the at least one magnetic sensor. The at least one magneticsensor may be operative to detect magnetic signals as the check crossesthe magnetic sensor. The data store may comprise a plurality ofpredetermined sets of amplitude values for Magnetic Ink CharacterRecognition (MICR) symbols of at least one MICR font. The at least oneprocessor may be in operative connection with the at least one magneticsensor, the at least one transport, and the data store. The at least oneprocessor may be operative to determine portions of the magnetic signalsproduced by the at least one magnetic sensor which corresponds to aplurality of MICR symbols. Each portion of the magnetic signal thatcorresponds to a MICR symbol may correspond to a MICR symbol waveform.For each MICR symbol waveform, the at least one processor may beoperative to identify minimum and maximum peaks in the MICR symbolwaveform; determine a set of amplitude values representative of theamplitudes of the identified peaks; and determine which MICR symbol ofthe at least one MICR font corresponds to the respective MICR symbolwaveform responsive to a comparison of the set of amplitude valuesdetermined for the respective MICR symbol waveform to each one of theplurality of predetermined sets of amplitude values for the MICR symbolsof the at least one MICR font stored in the data store. The at least oneprocessor may be operative to determine at least one set ofalphabetical, numerical and/or other characters based on the MICRsymbols determined from the MICR symbol waveforms. Such a set comprisingone or more such characters is referred to herein as a number forpurposes of brevity. In addition, the at least one processor may beoperative to send at least one message to a remote computer. The atleast one message may include data representative of the at least onenumber.

An exemplary embodiment may carry out a method of processing bankingcustomer transactions that may provide for the steps of: (a) receiving acheck in an automated banking machine including a card reader and cashdispenser, wherein the check may include a front face and a rear face;(b) moving a check across at least one magnetic sensor; (c) acquiringdigitized magnetic signals from the at least one magnetic sensor as thecheck moves across the at least one magnetic sensor, wherein themagnetic signal corresponds to a MICR symbol of at least one MICR font;(d) selecting data values corresponding to the digitized magneticsignals; (e) comparing the selected data values to predetermined featurevectors of each symbol of the MICR font; and (f) determining whichfeature vector corresponds to the selected data values. In some exampleembodiments the feature vector values and the selected data valuescorrespond to peak values, wherein the peak values may be positive ornegative. In other example embodiments the feature vector values and thepeak values correspond to peak amplitude and the data values maycorrespond to eight equally spaced locations in the time domain. In someexample embodiments the MICR font may correspond to the E-13B font. Inother example embodiments the feature vector values and the selecteddata values may correspond to distances between peak values. In otherexample embodiments the feature vector values and the data valuescorrespond to the six distances between peaks. In some exampleembodiments the distances between peaks may be either long or shortdistance values. In some example embodiments the MICR font correspondsto the CMC-7 font. In some example embodiments the digitized magneticsignal may correspond to a magnetic signal that has been sampled about100 times per MICR symbol and may correspond to a magnetic signal thathas been sampled about eleven times per magnetic signal peak.

An example embodiment may include an apparatus comprising a readerdevice, a magnetic sensor, a transport, a data store, and a processor.In some example embodiments the reader device is operative to read adata-bearing record, where the automated banking machine is operative toutilize the information read from the record by the reader device. Insome example embodiments the transport may be operative to move a checkacross the magnetic sensor. The magnetic sensor may be operative tosample magnetic signals as the check crosses the magnetic sensor,wherein the magnetic signal corresponds to a MICR symbol of a MICR font.In some example embodiments the data store may comprise a set ofpredetermined feature vectors that each correspond to a MICR symbol of aMICR font. In some example embodiments the processor may be in operativeconnection with the magnetic sensor, the transport, and the data store.In some example embodiments the processor may be operative to selectdata values corresponding to the sampled magnetic signals and comparethe selected data values to a predetermined feature vector of eachsymbol of the MICR font. In yet other example embodiment the processormay determine which feature vector corresponds to the selected datavalues. In some example embodiments the processor may use a Pearsoncorrelation function to determine which feature vector corresponds tothe selected data values.

An exemplary embodiment may carry out a method of processing bankingcustomer transactions that includes the steps of: (a) receiving a checkin an automated banking machine including a card reader and cashdispenser, wherein the check includes a front face and a rear face; (b)moving a check in a transport past top and bottom magnetic sensors; (c)acquiring digitized magnetic signals from the top and bottom magneticsensors as the check moves past the magnetic sensors; (d) throughoperation of a processor in the automated banking machine, determiningthe digitized magnetic signal regions corresponding to MICR symbols; (e)selecting data values corresponding to the digitized magnetic signals;(f) comparing the selected data values to a predetermined feature vectorof each symbol of the MICR font; (g) determining which feature vectorcorresponds to each of the selected data values or if the selected datavalues correspond to an invalid MICR symbol; and (h) determining whetherthe top or bottom magnetic sensors detected a valid MICR line ofsymbols. In some example embodiments the top magnetic sensor ispositioned at the top of the check as the check is moved in thetransport and the bottom magnetic sensor is positioned at the bottom ofthe check operative to read magnetic data on bottom of the check. Inother example embodiments the top and bottom sensors are both operativeto read magnetic data from the face of the check, facing magnetic sensoror magnetic data on the rear side of the check facing away from the topmagnetic sensor. In other example embodiment MICR data may be read onthe rear side of the check away from the sensors, by the magneticsensors sensing magnetic signals through the check. In some exampleembodiments the MICR font is an E-13B font. In some example embodimentsthe selected data values correspond to digitized magnetic signalwaveform peak values. In some example embodiments the selected peakvalues may be above a threshold value and the threshold value may beupdated after a fixed period of sampled data values.

Some example embodiments may carry out a method including the steps of:(a) receiving a check in an automated banking machine including a cardreader and cash dispenser, wherein the check includes a front face and arear face; (b) moving a check in a transport past top and bottommagnetic sensors; (c) acquiring digitized magnetic signals responsive tothe top and bottom magnetic sensors as the check moves past the magneticsensors; (d) through operation of a processor in the automated bankingmachine, determining the digitized magnetic signal regions correspondingto MICR symbols of the MICR font; (e) selecting data valuescorresponding to the digitized magnetic signals; (f) comparing theselected data values to a predetermined feature vector of each symbol ofthe MICR font, wherein each feature vector corresponds to a differentMICR symbol of the MICR font; (g) determining which feature vectorcorresponds to each of the selected data values; (h) through operationof a processor in the automated banking machine, causing the opticalsensors to capture images of the check; and (h) responsive to (e) and(f) determining whether the top or bottom magnetic sensors detected avalid MICR line of symbols. In some example embodiments the MICR font isa CMC-7 font. In some example embodiments the selected data valuescorrespond to the distance between adjacent waveform peak values.

In some example embodiments an automatic banking machine may read abanking check in any of the four possible positional orientations withan apparatus comprising: a reader device, a top magnetic sensor, abottom magnetic sensor, a transport and a processor. In some exampleembodiments the reader device may be operative to read a data bearingrecord, and the automated banking machine may be operative to utilizethe information read from the record by the reader device. In someexample embodiments the top magnetic sensor may be operative to bepositioned at the top of the check as the check is moved in thetransport and the bottom magnetic sensor may be positioned near thebottom of the check operative to read magnetic data on the bottom of thecheck. In some example embodiments the top and bottom sensors are bothoperative to read magnetic data from the face of the check facing themagnetic sensors and may also be operative to read magnetic data on therear side of the check facing away from the top and bottom magneticsensors. In some example embodiments when a sensor is reading data onthe rear side of the check away from the sensors, the magnetic signalsmay be sensed through the check. In some example embodiments theprocessor may be operative to cause a check in a transport to moveacross the at least top and bottom magnetic sensors. In some exampleembodiments the processor may cause the magnetic sensors to acquiredigitized magnetic signals as the check moves past the magnetic sensors.In some example embodiments the processor may determine the digitizedmagnetic signal regions that correspond to MICR symbols of a MICR fonteither for the digital magnetic signals corresponding with the topmagnetic sensor or for the digital magnetic signals corresponding withthe bottom magnetic sensor. In some example embodiments the processormay be operative to determine, for each the magnetic signal regions, howmany valid and invalid MICR signals are detected with the top magneticsensor and how many valid and invalid MICR symbols are detected with thebottom magnetic sensor and to determine whether the top or bottommagnetic sensors detected a valid MICR line of symbols. In some exampleembodiments the MICR font is an E-13B font. In some example embodimentsthe processor may also be operative to select data values correspondingto the digitized magnetic signals and to compare the selected datavalues to predetermined feature vectors of each symbol of the MICR font.In some example embodiments the processor may determine which featurevector corresponds to each of the selected data values or if theselected data values may correspond to an invalid MICR symbol. In someexample embodiments the selected data values may correspond to digitizedmagnetic signal waveform peak values and the peak values may be above athreshold value. In some example embodiments the threshold may beupdated after a fixed period of data values.

Some example embodiments an automatic banking machine may read a bankingcheck in any of the four possible positional orientations with anapparatus comprising: a reader device, a top and a bottom magneticsensor, a transport and a processor. In some example embodiments thereader device is operative to read a data bearing record withinformation that the automated banking machine may use. In some exampleembodiments the top and bottom magnetic sensors may be operative todetect magnetic signals as the check moves across the magnetic sensors,and the top magnetic sensor may be operative to be positioned at the topof the check as the check is moved in the transport and the bottommagnetic sensor may be positioned at the bottom of the check to readmagnetic data on the bottom of the check. In some example embodimentsthe sensors may read magnetic data from the face of the check facingmagnetic sensor or magnetic data on the rear side of the check facingaway from the magnetic sensors. In some example embodiments when readingdata on the rear side of the check away from the sensors, the magneticsignals may be sensed through the check. In some example embodiments theprocessor may be in operative connection with the magnetic sensor, thetransport, and the data store. In some example embodiments the processormay be operative to cause a check in the transport to move across thesensors, cause the magnetic sensors to acquire digitized magneticsignals and determine the digitized magnetic signal regionscorresponding to MICR symbols of a MICR font. In some exampleembodiments the processor may be further operative to determine whichMICR symbols correspond to the digitized magnetic signal regions, causethe optical sensors to capture images of the check and determine whetherthe top or bottom magnetic sensors detected a valid MICR line ofsymbols. In some example embodiments the MICR font is a CMC-7 font. Insome example embodiments the processor is further operative to selectdata values corresponding to the digitized magnetic signals, comparethose values to predetermined feature vectors of each symbol of the MICRfont which corresponds to a different MICR symbol and to determine whichfeature vector corresponds to each of the selected data values. In someexample embodiments the selected data values may correspond to thedistance between adjacent waveform peak values and the selected peakvalues may be above a threshold value.

Some example embodiments may carry out a method of recognizing peakvalues comprising the steps of: (a) receiving a check in an automatedbanking machine including a cash dispenser; (b) moving a check across amagnetic sensor; (c) acquiring electrical signals from the magneticsensor as the check moves across the magnetic sensor, wherein themagnetic sensor may be operative to cause generation of a plurality ofdigital magnetic samples; (d) determining a plurality of magnetic signalportions which may correspond to one of a plurality of MICR symbols of aMICR font; (e) identifying a plurality of peaks of each of the pluralityof magnetic signal portions; (f) determining for each of the pluralityof peaks the weight of each peak; (g) accessing a data store including aplurality of predetermined sets of data values for MICR symbols of MICRfont; (h) correlating each of the plurality of magnetic signal portionsusing the weights determined in (f) with each of the MICR symbols of theMICR font; and (i) determining which MICR symbol of the MICR fontcorresponds to each magnetic signal portion by selecting the MICR symbolof the MICR font that has the highest correlation value calculated in(h). In some example embodiments in (f) the weight of a peak isdetermined by taking a given distance P and calculating the datacorresponding to left and right areas under a graphical representationof a magnetic signal portion, where the left area corresponds to theleft area from the peak center to a distance P on the left side of thepeak, and where the right area corresponds to the area from the peakcenter to a distance P right of the peak center, wherein the weight of apeak is defined as two times the smaller of the left or right peakareas. In some example embodiments digitally sampled signals may beacquired in (c). In some example embodiments a baseline correction maybe subtracted from each raw digitized magnetic signal, where thebaseline correction may be an average value. In some example embodimentsthe average value may be the average value over a corresponding fixedrange of the digital magnetic samples. In some example embodiments theplurality of peaks determined in (e) may correspond to a set ofamplitude values representative of the amplitudes of the identifiedpeaks and may be ordered in a sequence corresponding to the respectivepositions in time along the magnetic signal portions. In some exampleembodiments the possible peaks may be determined at eight fixedlocations equally spaced apart. In some example embodiments a processormay cause the check to be stored in the automated banking machine. Insome example embodiments predetermined sets of data values in (g) may bepeak weight values or peak amplitude values. In some example embodimentsthe at least one MICR font may include a MICR E-13B font. In yet otherexample embodiments in (h) a Pearson correlation may be used tocorrelate each of the magnetic signal portions to one of the MICRsymbols.

In some example embodiments an automatic banking machine may detect MICRpeak symbols with an apparatus comprising a reader device, a magneticsensor, a transport, a data store and a processor. In some exampleembodiments the processor may be in operative connection with the readerdevice, magnetic sensor, the transport, and the data store. In someexample embodiments the processor may be operative to determine amagnetic signal portion of the acquired magnetic signal whichcorresponds to a MICR symbol, identify a peak of the magnetic signalportion and determine for each of the plurality of peaks the weight ofeach peak. In some example embodiments the processor may be operative tocompare the plurality of peaks of a magnetic signal portion to each oneof the predetermined sets of peak values for each MICR symbol of a MICRfont and to determine which MICR symbol corresponds to the magneticsignal portion. In some example embodiments the data store may comprisea plurality of predetermined sets of values for MICR symbols of at leastone MICR font. In some example embodiments the weight of a peak maycorrespond in a graphical representation to at least a portion of thearea under the magnetic signal portion. In some example embodiments theweight of a peak may be determined by taking a given distance P andcalculating the left and right areas under a magnetic signal portion,wherein the left area may correspond to the left area from the peakcenter to a distance P on the left side of the peak, wherein the rightarea may correspond to the area from the peak center to a distance Pright of the peak center, wherein the weight of a peak may be defined astwo times the smaller of the left or right peak areas.

In some example embodiments the magnetic sensor may be operative tocause the machine to digitally sample the acquired magnetic signals. Insome example embodiments a baseline correction may be subtracted fromeach raw digitized magnetic signal, where the baseline correction may bean average value and where the average value may be an average valueover a corresponding fixed range of the digitally sampled signals. Insome example embodiments the plurality of peaks may correspond to a setof amplitude values representative of the amplitudes of the identifiedpeaks and are ordered in a sequence corresponding to the respectivepositions in time along the magnetic signal portions. In some exampleembodiments possible peaks may be determined at eight locations equallyspaced apart. In some example embodiments the processor may cause thecheck to be stored in the automated banking machine.

In some example embodiments the predetermined sets of data values may bepeak weight values and may correspond to peak amplitude values. In someexample embodiments the at least one MICR font may include a MICR E-13Bfont. In some example embodiments a Pearson correlation may be used tocorrelate the magnetic signal to the MICR symbols.

Some example embodiments may carry out a method for detection MICR peakswith steps that may comprise: (a) receiving a check in an automatedbanking machine including a cash dispenser (b) moving a check across amagnetic sensor; (c) acquiring electrical signals from the magneticsensor as the check moves across the magnetic sensor, wherein themagnetic sensor may be operative to cause generation of a plurality ofdigital magnetic samples; (d) determining a first magnetic signalportion which corresponds to one of a plurality of MICR symbols; (e)identifying a plurality of peaks in the first magnetic signal portion;(f) determining for each of the plurality of peaks, a zone ofconsecutive magnetic samples; (g) determining an anchor depth for eachof the plurality of peaks; (h) determining for each of the plurality ofpeaks the weight of each peak (i) determining for each of the pluralityof peaks the cut series of each peak (j) determining for each of theplurality of peaks a peak cut; (k) comparing for each of the pluralityof peaks determined in (e) with the corresponding anchor depth, peakcut, cut series, peak weight and peak amplitude to determine if the peakis a valid peak and discarding invalid peaks; (l) for the magneticsignal portion, comparing the plurality of valid peaks to each one of aplurality of predetermined sets of peak values for each MICR symbol of aMICR font; and (m) for the magnetic signal portion, determining whichMICR symbol of the MICR font corresponds to the first magnetic signalportion responsive to (l).

Some example embodiments may include an apparatus for detecting MICRpeaks used with an automatic banking machine apparatus comprising: areader device, magnetic sensor, transport, a data store and a processor.In some example embodiments the processor may be in operative connectionwith the magnetic sensor, the transport, and the data store. Theprocessor may be further operative to determine a first magnetic signalportion of acquired magnetic signals which are electrical signals thatcorrespond to a MICR symbol and to identify a plurality of minimum andmaximum peaks of the magnetic signal portion. In some embodiments theprocessor may be further operative to determine a set of amplitudevalues representative of the amplitudes of the identified peaks. In someexample embodiments the processor may be operative to determine for eachof the peaks, an anchor depth, cut series, weight, and peak cut. In someexample embodiments the processor is further operative to compare eachof the plurality of peaks with the corresponding anchor depth, cutseries, weight, and peak cut of each peak to determine if the peak is avalid peak and to discard invalid peaks. In some example embodiments theprocessor may be operative to read from the data store a plurality ofpredetermined sets of peak values for each MICR symbol of a MICR fontand to compare the plurality of peaks of the first magnetic signalportion to predetermined sets of peak values for the MICR symbols. Insome example embodiments the processor may be operative to determinewhich MICR symbol of the MICR font corresponds to the first magneticsignal portion.

In some example embodiments the reader device is operative to read adata-bearing record, where the automated banking machine may beoperative to utilize the information read from the record by the readerdevice. In some example embodiments the transport may be operative tomove a check across the magnetic sensor, where the magnetic sensor maybe operative to generate electrical signals which may also be referredto herein as magnetic signals as the check crosses the magnetic sensor.In some example embodiments the data store may comprise a plurality ofpredetermined sets of amplitude values for MICR symbols of a MICR font.In some example embodiments a zone of consecutive magnetic samples maybe selected that correspond to locations that are equal distance apartin the time domain. In some example embodiments the zone may contain2*hw+1 consecutive magnetic samples where hw represents the peak halfwidth such that there may be exactly hw magnetic samples before andafter the identified peak. In some example embodiments the anchor depthmay be determined by measuring the value of the first magnetic signalportion at distance hw on each side of the center of the peak, whereinthe anchor depth is defined as the smaller magnitude of the two magneticvalues at a distance hw on each side of the center of the peak. In someexample embodiments a baseline correction may be subtracted from eachraw digitized magnetic signal and the baseline correction may be anaverage value. In some example embodiments the average value may be theaverage value over a corresponding fixed range of raw digitized magneticsignals. In some example embodiments when a new raw magnetic sample maybe acquired and added to the fixed raw magnetic sample range, the oldestraw magnetic sample from the fixed raw magnetic sample range may beremoved and the average value may be recalculated. In some exampleembodiments the weight of a peak may be determined by taking a givendistance P and calculating the left and right areas under the firstmagnetic signal portion, where the left area corresponds to the leftarea from the peak center to a distance P on the left side of the peak,where the right area corresponds to the area from the peak center to adistance P right of the peak center and where the weight of a peak isdefined as two times the smaller of the left or right peak areas. Insome example embodiments the cut series of a peak may be determined bysubtracting the product of the average of the endpoints and the zonelength L from the total area under the first magnetic signal portionbetween the two endpoints, wherein the endpoints are the points on eachside of the peak center at distance L/2 from the peak center. In someexample embodiments the peak cut may be the area under the firstmagnetic signal portion and a straight cord extending from the twomagnetic signal portion values at a distance X from the peak center oneach side of the peak center. In some example embodiments the value ofhw may be about 5.

Some example embodiments may carry out a method for detection of MICRpeaks with optical symbol recognition assistance with steps that maycomprise: (a) receiving a check in an automated banking machineincluding a card reader and cash dispenser; (b) moving the check acrossmagnetic sensor; (c) acquiring samples of magnetic signal data with themagnetic sensor as the check moves across the magnetic sensor; (d)determining from the samples of magnetic signal data, at least one setof data corresponding to MICR symbols for the magnetic symbols on thecheck; (e) determining a correspondence between the set of datadetermined in (d) and a predetermined MICR symbol of a MICR font; (f)capturing an optical image of the check; (g) cropping the MICR symbolsfrom the rest of the check; (h) applying a contrast boost to the MICRsymbols; (i) de-skewing the MICR symbol; (j) determining acorrespondence between the optical image and a predetermined MICR symbolof a MICR font; and (k) combining the magnetic symbol correspondence ofstep (e) with the optical symbol correspondence in step (j) to determinea final symbol associated with each of the plurality of magnetic symbolson the check. Some example embodiments may consist of the further stepof (l) performing a positional correlation with the magnetic and opticalrepresentations of the magnetic symbols beginning at the symbol that hasthe highest combined magnetic and optical confidence levels andperforming the correlation from that symbol in the forward and reversedirections one symbol at a time until a determination has been made asto what may be the correct value of each symbol. In some exampleembodiments a magnetic confidence level may be associated with eachsymbol associated with the plurality of magnetic symbols on a check foreach step (e) and an optical confidence level is associated with eachsymbol associated with the plurality of magnetic symbols on a check foreach step (j), wherein the confidence levels may be indications of howlikely each of the plurality of symbols is associated with the correctsymbol, wherein (k) may be responsive to the confidence levels. In someexample embodiments in step (k) the symbol associated with the pluralityof symbols on a check may be the one with the highest magnetic oroptical confidence level. In some example embodiments the samples ofmagnetic signal data may correspond to digital magnetic samples, wherethe sets of data each consist of a range of digital magnetic samplesthat are evenly spaced in the time domain represented by x(i) where x isthe value of the digital magnetic sample at location i. In some exampleembodiments when the magnetic confidence level may be below an opticalconfidence level, each data value in the set of data is shifted by oneof: x(i+1) and x(i−1) and the magnetic confidence value may berecalculated. In some example embodiments the sets of data may be datafor amplitude values. In some example embodiments the sets of data eachmay contain about 100 data values. In some example embodiments theconfidence level may correspond to how well the peaks of magnetic datacorrespond to the peaks of the predetermined MICR symbols. In someexample embodiments the confidence level may correspond to how well thedistances between peak centers of the magnetic data correspond to thedistances between peak centers of the predetermined MICR symbol of aMICR font.

Some example embodiments may include an apparatus for detecting MICRpeaks with an optical recognition assist used with an automatic bankingmachine apparatus comprising: a reader device, a transport, a data storeand a processor. In some example embodiments the reader device may beoperative to read a data-bearing record and the automated bankingmachine may be operative to utilize the information read from the recordby the reader device. In some example embodiments the optical imagingdevice may be operative to capture optical images. In some exampleembodiments the transport may be operative to move a check across themagnetic sensor. In some example embodiments the data store may comprisea plurality of predetermined sets of amplitude values for MICR symbolsof a MICR font. In some example embodiments the processor may be inoperative connection with the magnetic sensor, the transport, and thedata store. In some example embodiments the processor may be operativeto determine from the samples of magnetic signal data, sets of magneticsymbol data corresponding to MICR symbols for each of the plurality ofmagnetic symbols on the check. In some example embodiments the processormay further be operative to determine a symbol corresponding to each ofthe plurality of magnetic symbol waveforms on the check by correlatingeach of the sets of magnetic symbol data with a predetermined MICRsymbol of a MICR font. In some example embodiments the processor may beoperative to operate the optical imaging device to capture an opticalimage of the check. In some example embodiments the processor may useoptical symbol recognition methods to determine a symbol correspondingto a MICR symbol on the check by associating an image of each symbolcaptured with the optical imaging device with a predetermined MICRsymbol. In some example embodiments the processor may evaluate themagnetic and optical symbols to determine whether the magnetic oroptical symbol corresponds best with the corresponding predeterminedMICR symbol. In some example embodiments the processor may be furtheroperative to determine a magnetic confidence level for each symboldetermined to correspond to each of the plurality of magnetic symbols onthe check by correlating each of the sets of magnetic symbol data witheach predetermined MICR symbol of a MICR font and assigning a confidencelevel with the highest correlating magnetic symbol data. In some exampleembodiments the processor may be further operative to determine anoptical confidence level for each symbol determined to corresponding toeach of the plurality of symbols on the check by correlating an image ofeach symbol captured with the optical imaging device to eachpredetermined MICR symbol of a MICR font and assigning a confidencelevel with the highest correlating optical symbol data. In some exampleembodiments the confidence levels may be indications of how likely eachof the plurality of symbols is associated with the correct symbol. Insome example embodiments the symbol associated with the plurality ofsymbols on a check may be the one with the highest magnetic or opticalconfidence level. In some example embodiments the samples of magneticsignal data may correspond to digital magnetic samples, wherein the setsof data each consist of a range of digital magnetic samples that areevenly spaced in the time domain represented by x(i) where x is thevalue of the digital magnetic sample at location i. In some exampleembodiments when the magnetic confidence level may be below an opticalconfidence level, each data value in the set of magnetic symbol data isshifted by one of: x(i+1) and x(i−1) and the magnetic confidence valuemay be recalculated. In some example embodiments the sets of data may besets of data for amplitude values. In some example embodiments the setsof data each may contain about 100 data values. In some exampleembodiments the confidence level corresponds to how well the peaks ofmagnetic data correspond to the peaks of the predetermined MICR symbolof MICR font. In some example embodiments the confidence level maycorrespond to how well the distances between peak centers of themagnetic data correspond to the distances between peak centers of thepredetermined MICR symbol of MICR font. In some example embodiments theprocessor may be further operative to use an image captured from theoptical imaging device to crop the MICR symbols from the rest of thecheck, apply a contrast boost to the cropped MICR symbols and to de-skewthe MICR symbols. In some example embodiments the processor may befurther operative to perform a positional correlation with the magneticand optical representations of the magnetic symbols. The correlation maybegin at the symbol that has the highest combined magnetic and opticalconfidence levels. Next, a correlation adjacent to that symbol may beperformed in sequence on subsequent signals in the forward and reversedirections one symbol at a time until a determination has been made asto what is the correct value of each symbol.

Some example embodiments of a method to detect MICR symbols maycomprise: (a) receiving a check in an automated banking machineincluding a card reader and cash dispenser, wherein the check mayinclude a front face and a rear face; (b) moving a check across amagnetic sensor; (c) producing raw digitized magnetic signals from theelectrical signals produced by the magnetic sensor as the check movesacross the magnetic sensor; (d) applying a baseline correction to theraw digitized magnetic signals to produce baseline corrected magneticsignals; (e) filtering the baseline corrected magnetic signals to boostthe signal to noise ratio by attenuating high frequency noise to producefiltered magnetic signals; (f) determining the first magnetic signalportion of the filtered magnetic signals that corresponds to a MICRsymbol of a MICR font; (g) identifying a plurality of peaks of the firstmagnetic signal portion; (h) comparing the set of peaks determined in(g) for the first magnetic signal portion to each one of a plurality ofpredetermined sets of amplitude values for MICR symbols of the MICRfont; (i) for the first magnetic signal portion, determining which MICRsymbol of the MICR font corresponds to the first magnetic signal portionresponsive to (h); (j) determining at least one number from the MICRsymbols determined in (i); (k) sending at least one message to a remotecomputer, wherein the at least one message includes data representativeof the at least one number determined in (j); (l) causing the check tobe stored in the automatic banking machine; (m) determining for thefirst magnetic signal portion a confidence level for each comparisonbetween the set of peak values determined in (g) for first magneticsignal portions to the plurality of predetermined sets of peak valuesfor each MICR symbol of the MICR font; and (n) determining with whichpredetermined MICR symbol the first magnetic signal portion has thehighest confidence level.

In some example embodiments the baseline correction may be an averagevalue and may be subtracted from each raw digitized magnetic signal and,where the average value may be the average value over a correspondingfixed range of raw digitized magnetic signals. In some exampleembodiments when a new raw magnetic sample may be acquired and added tothe fixed raw magnetic sample range, the oldest raw magnetic sample fromthe fixed raw magnetic sample range is removed and the average value maybe recalculated. In some example embodiments the plurality of peaks maycorrespond to a set of amplitude values representative of the amplitudesof the identified peaks and may be ordered in a sequence correspondingto the respective positions in time along the filtered magnetic signalportion. In some example embodiments the confidence levels may bedetermined with a Pearson correlation calculation carried out by amicroprocessor operating on the peak values. In some example embodimentsthe magnetic sensor may comprise a plurality of sensor elements arrangedconsecutively along at least one column and magnetic signals from eachof the sensor elements may be acquired as the check moves across themagnetic sensor elements. In some example embodiments the check may besampled with the magnetic sensor about every 63.5 micro-seconds and thesample may be converted to an eight bit unsigned integer value. In someexample embodiments the check may be transported on a transport at thespeed of about 500 mm/s. In some example embodiments the correctedmagnetic signals may be filtered with a Bessel Infinite Impulse ResponseFilter (IIF) and the filter may be a 10th order filter. In yet otherexample embodiment the filtering may be performed with a processor inthe automated banking machine digitally filtering the corrected magneticsignal values.

Some example embodiments may include an apparatus that filters andrecognizes MICR symbols that may comprise a reader device, a magneticsensor, a transport and a processor. In some example embodiments thereader device may be operative to read a data-bearing record, where themachine is operative to utilize the information read from the readerdevice. In some example embodiments a first filter may be operative toapplying a baseline correction to the raw digitized magnetic signals toproduce a baseline corrected magnetic signal. In some exampleembodiments a second filter may be operative to filter the base linecorrected magnetic signals to boost the signal to noise ratio toattenuate high frequency noise and produce a filtered magnetic signal.In some example embodiments one or more of the filters may be a discretefilter or a filter digitally implemented in the processor. In someexample embodiments the processor may be in operative connection withthe magnetic sensor, the transport, and the data store. In some exampleembodiments the processor may be operative to determine a first magneticsignal portion of the filtered magnetic signal which corresponds to aMICR symbol and to identify minimum and maximum peaks in the firstmagnetic signal portion. In some example embodiments the processor maybe operative to determine a set of amplitude values representative ofthe amplitudes of the identified peaks and may determine which MICRsymbol corresponds to the first magnetic signal portion. In some exampleembodiments the automated banking machine includes a cash dispenser anda deposit accepting apparatus that may include the magnetic sensor andthe transport. In some example embodiments the deposit acceptingapparatus may include a storage area and the processor may be operativeto cause the transport to move the check to the storage area.

Some example embodiments may recognize magnetic symbols using a methodincluding flagging peak values located in between sample locations andmay comprise: (a) receiving a check in an automated banking machineincluding a cash dispenser; (b) moving a check across at least onemagnetic sensor; (c) sampling electrical signals from the at least onemagnetic sensor as the check moves across the at least one magneticsensor, wherein the sample magnetic signals include digital magneticsamples; (d) through operation of at least one processor in theautomated banking machine, generating a plurality of data valuescorresponding to magnetic waveform peak values in the digital magneticsamples corresponding to a MICR symbol; (e) associating the peaks with acorresponding feature vector position and a corresponding feature vectorposition value to produce at least one sample feature vector, where thefeature vector positions correspond to fixed locations in the timedomain; (f) comparing the at least one sample vector with each of thefeature vectors in the data store; and (g) determining to which MICRsymbol the sample vector most likely corresponds.

In some example embodiments the check may have a front face and a rearface and may have symbols comprised of magnetic ink corresponding toMICR symbols. In some example embodiments each MICR symbol maycorrespond to a feature vector that may have eight feature vectorposition values. In some example embodiments a data store may store aset of standard feature vectors that correspond to the MICR symbols. Insome example embodiments when a first peak is in between two featurevector positions the first peak may be associated with a first adjacentfeature vector position and a second adjacent feature vector position,wherein a first sample vector may be produced with the first peakassociated with a first adjacent feature vector position and a secondsample vector may be produced with the first peak associated with asecond adjacent feature vector position. In some example embodiments thefeature position vector values and the peak values may correspond topeak amplitude and the feature position vector values and the peakvalues may correspond to eight sampling locations. In some exampleembodiments a Pearson correlation may be used to correlate each of themagnetic signal portions to one of the MICR symbols.

Some example embodiments may recognize magnetic symbols using a methodincluding flagging peak values located in between sample locationscomprising: (a) receiving a check in an automated banking machineincluding a cash dispenser; (b) moving a check across at least onemagnetic sensor; (c) sampling signals from the at least one magneticsensor as the check moves across the at least one magnetic sensor,wherein the sampled signals are digital magnetic samples; (d)identifying magnetic waveform peak locations in the digital magneticsamples corresponding to a MICR symbol; (e) determining distancesbetween adjacent peak locations; (f) associating the distances betweenpeaks with a corresponding feature vector position value to produce asample vector; (g) correlating the sample vector with each of thefeature vectors of a MICR font; (h) determining to which MICR symbol thesample vector most likely corresponds; (i) causing the check to bestored in the automated banking machine.

In some example embodiments where only six valid peaks are detected in(d) this may further include determining the longest distance betweenadjacent peak locations that may be made and estimating the location ofthe missing peak to be the middle of the longest distance of the peaks.Some example embodiments may have seven valid peaks in each MICR symboland there may have six valid distances between peaks. In some exampleembodiments the distances between immediately adjacent peaks correspondto one of short or long relative distances. In some example embodimentsthe MICR font corresponds to the CMC-7 font. In some example embodimentsa Pearson correlation may be used to correlate each of the magneticsignal portions to one of the MICR symbols. In some example embodimentsthe magnetic signals may be sampled about 100 times per symbol. In someexample embodiments when eight peaks are detected a determination may bemade as to which of the eight peaks is likely an invalid peak and thepeak with the lowest amplitude may be determined to be an invalid peak.

Some example embodiments may include an apparatus that recognizes MICRsymbols comprising: a reader device, a magnetic sensor, a transport, adata store and a processor. In some example embodiments the processormay be in operative connection with the magnetic sensor, the transport,and the data store. In some example embodiments the processor may beoperative to identify magnetic waveform peak values in the digitalmagnetic samples taken by the magnetic sensor that may correspond to aMICR symbol. In some example embodiments the processor may be operativeto associate the peaks with a corresponding feature vector positionvalue to produce a sample vector and the processor may be able tocorrelate the sample vector with each of the feature vectors of a MICRfont. In some example embodiments the processor may be operative todetermine to which MICR symbol the sample vector may most likelycorrespond. In some example embodiments the reader device may beoperative to read a data-bearing record, where the automated bankingmachine is operative to utilize the information read from the record bythe reader device. In some example embodiments the transport may beoperative to move a check across the magnetic sensor, wherein themagnetic sensor may be operative to sample magnetic signals as the checkcrosses the magnetic sensor. In some example embodiments the data storemay comprise feature vectors corresponding to each MICR symbol of a MICRfont.

In some example embodiments when the processor determines a first peakis in between two feature vector positions, the processor may associatethe first peak with a first adjacent feature vector position and asecond adjacent feature vector position. In some example embodiments theprocessor may be operative to produce a first sample vector with thefirst peak associated with a first adjacent feature vector elementposition and to produce a second sample vector with the first peakassociated with a second adjacent feature vector element position.

Some example embodiments may include an apparatus that recognizes MICRsymbols that may comprise: a reader device, a magnetic sensor, atransport, a data store and a processor. In some example embodiments theprocessor may be in operative connection with the magnetic sensor, atransport, and the data store. In some example embodiments the processormay be operative to identify magnetic waveform peak values of thedigital magnetic samples taken with the magnetic sensor that correspondsto a MICR symbol. In some example embodiments the processor may beoperative to associate the peaks with a corresponding feature vectorposition value to produce a sample vector. In some example embodimentsthe processor may be able to determine distances between adjacent peaklocations and may be able to compare the sample vector with each of thefeature vectors of the MICR font. In some example embodiments theprocessor may be operative to determine which MICR symbol the samplevector generally corresponds. In some example embodiments the readerdevice may be operative to read a data bearing record, where theautomated banking machine is operative to utilize the information readfrom the record read by the reader device. In some example embodimentsthe transport may be operative to move a check across the magneticsensor, wherein the magnetic sensor may be operative to sampleelectrical signals as the check crosses the magnetic sensor. In someexample embodiments the data store comprises feature vectors that maycorrespond to each MICR symbol of a MICR font. In some exampleembodiments there may be seven valid peaks in each MICR symbol and theremay be six valid distances between peaks. In some example embodimentsthe distances between peaks may correspond to a short or long distance.In some example embodiments when only six valid peaks are detected theprocessor may be further operative to determine the longest distancebetween adjacent peak locations and may be operable to estimate thelocation of the missing peak. In some example embodiments the distancesbetween peaks correspond to one of short or long distances. In someexample embodiments when eight peaks are detected the processor may beoperable to determine which of the eight peaks may be likely an invalidpeak. In some example embodiments the peak with the lowest amplitude maybe determined to be an invalid peak.

Some example embodiments may recognize magnetic symbols using magneticand optical recognition techniques and may comprise: (a) receiving acheck in an automated banking machine including a cash dispenser; (b)optically scanning a MICR symbol on the check in a plurality of firstparallel directions; (c) constructing a two dimensional constructedwaveform, where the vertical axis corresponds to the optical intensityof the ink of the scanned MICR symbol and the horizontal axiscorresponds to the location of the scanned optical intensity; (d)comparing the constructed waveform to the set of predeterminedwaveforms; and (e) determining to which of the waveforms in the set ofpredetermined waveforms the constructed waveform corresponds. In someexample embodiments the MICR symbols may correspond to a predeterminedwaveform. In some example embodiments the predetermined waveform mayhave a vertical axis that corresponds to the optical intensity of a lineof ink extending transversely across the image of a MICR symbol and ahorizontal axis corresponds to the location of the optical intensity. Insome example embodiments the predetermined waveforms for each MICRsymbol form a set of predetermined waveforms. In some exampleembodiments the check may be optically scanned in the horizontal orvertical direction. In some example embodiments the symbol may beoptically scanned in about eleven (11) parallel scan lines. In someexample embodiments the correlation of the constructed waveform may beperformed with a Pearson correlation. In some example embodiments theMICR font may be one of E-13B or CMC-7 font.

Some example embodiments may recognize magnetic symbols using magneticand optical recognition and may comprise: (a) receiving a check in anautomated banking machine including a cash dispenser; (b) capturing afirst optical image of the entire check; (c) optically scanning theentire check in a plurality of horizontal directions the length of thecheck; (d) determining the vertical position as to where the line ofMICR symbols are on the check, wherein the determination is maderesponsive to (c); (e) cropping the MICR symbols from the check from thefirst optical image; (f) contrast boosting the cropped MICR symbols toproduce a second optical image; (g) scanning of the cropped MICR symbolsin a plurality of transverse directions; (h) determining the position ofthe MICR symbol on the second image; and (i) providing the location ofthe MICR symbol on the second image to software that is operative todetermine which MICR symbol corresponds with the MICR symbol on thesecond image. In some example embodiments the MICR symbols maycorrespond to a predetermined waveform. In some example embodiments thepredetermined waveform may have a vertical axis that corresponds to theoptical intensity of a line of ink extending transversely across theimage of a MICR symbol and a horizontal axis that may correspond to thelocation of the optical intensity. In some example embodiments thepredetermined waveforms for each MICR symbol form a set of predeterminedwaveforms. In some example embodiments the transverse directions may bevertical or horizontal directions corresponding to the orientation ofthe check. In some example embodiments the MICR symbol may be scannedwith about 11 equally spaced vertical scans. In some example embodimentsthe X and Y coordinates of the MICR symbol on the second image may beprovided to the symbol recognition software. In some example embodimentsthe MICR font may be E-13B or CMC-7 font. In some example embodimentsthe processor may be operative responsive to (c) to construct datacorresponding to a two-dimensional first waveform, where the verticalaxis may correspond to the optical intensity of the ink of the scannedMICR symbol and the horizontal axis may correspond to the location ofthe scanned optical intensity. In some example embodiments the processormay be operative responsive to (g) to construct data corresponding to atwo-dimensional second waveform, where the vertical axis may correspondto the optical intensity of the ink of the scanned MICR symbol and thehorizontal axis may correspond to the location of the scanned opticalintensity. In some example embodiments the determination in (d) may beresponsive to the first waveform and the determination in (h) may beresponsive to the second waveform.

Some example embodiments may recognize MICR symbols with an apparatusthat includes a reader device, an optical sensor, a transport, a datastore and a processor. In some example embodiments the processor may bein operative connection with the optical sensor, a transport, and thedata store. In some example embodiments the automated banking machinemay be operative to utilize the information read from the record by thereader device. In some example embodiments the optical scanner may beoperative to scan a check in a plurality of scan lines. In some exampleembodiments the transport may be operative to move a check to theoptical scanner. In some example embodiments the processor may beoperative to cause a check received in the automatic banking machine tobe moved on the transport to the optical scanner and the check may havesymbols written with magnetic ink corresponding to MICR symbols. In someexample embodiments each MICR symbol corresponds to a predeterminedwaveform, where the predetermined waveform may correspond in a graphicalrepresentation to a representation which includes a vertical axis thatcorresponds to the optical intensity of a line of ink extendingtransversely across the image of a MICR symbol and a horizontal axiswhich may correspond to the location of the optical intensity. Thepredetermined waveforms for each MICR symbol may form a set ofpredetermined waveforms. In some example embodiments the processor maybe further operative to cause the optical scanner to optically scan theMICR symbol on the check in a plurality of first parallel directions. Insome example embodiments the processor may be further operative toconstruct a two-dimensional waveform, where the vertical axis maycorrespond to the optical intensity of the ink of the scanned MICRsymbol and the horizontal axis may correspond to the location of thescanned optical intensity. In some example embodiments the processor maybe further operative to correlate the constructed waveform to the set ofpredetermined waveforms and to determine to which of the waveforms inthe set of predetermined waveforms the constructed waveform maycorrespond.

Some example embodiments may recognize MICR symbols with an apparatusthat includes a reader device, an optical sensor, a transport, a datastore and a processor. In some example embodiments the processor may bein operative connection with the optical sensor, a transport, and thedata store. In some example embodiments the optical scanner may beoperative to scan a check in a plurality of scan lines. In some exampleembodiments the transport may be operative to move a check to theoptical scanner. In some example embodiments the processor may beoperative to cause a check received in the automatic banking machine tobe moved on the transport to the optical scanner. In some exampleembodiments each MICR symbol may correspond to a predetermined waveform,where the predetermined waveform may have a vertical axis that maycorrespond to the optical intensity of a line of ink extendingtransversely across the image of a MICR symbol and a horizontal axiswhich may correspond to the location of the optical intensity. In someexample embodiments the processor may further be operative to cause theoptical scanner to optically scan the MICR symbol on the check in aplurality of first parallel directions. In some example embodiments theprocessor may be operative to cause the optical scanner to opticallyscan the entire check in a plurality of horizontal directionscorresponding to the length of the check. In some example embodimentsthe processor may be operative to determine the vertical position wherethe line of MICR symbols are located on the check, where thedetermination may be made responsive to the plurality of horizontalscans made the length of the check. In some example embodiments theprocessor may be operative to crop the MICR symbols from the image data.In some example embodiments the processor may be operative to contrastboost the cropped MICR symbols to produce a second optical image. Insome example embodiments the processor is operative to scan the croppedMICR symbols in a plurality of transverse directions. In some exampleembodiments the processor is operative to determine the position of theMICR symbol on the second optical image and provide the location of MICRsymbol on the second image to software that may be operative todetermine which MICR symbol corresponds with the MICR symbol on thesecond image.

In an example embodiment, the described apparatus may correspond to anautomated banking machine including a cash dispenser and a depositaccepting apparatus. The deposit accepting apparatus includes the atleast one magnetic sensor, the at least one optical sensor and the atleast one transport.

A further example embodiment may carry out a method. The method mayinclude (a) moving a check across at least one magnetic sensor and (b)acquiring electrical signals from the at least one magnetic sensor asthe check moves across the at least one magnetic sensor. In additionthis exemplary method comprises (c) through operation of at least oneprocessor in the automated banking machine, determining the portions ofthe signals which correspond to one of a plurality of MICR symbols. Eachportion of the magnetic signals that correspond to a MICR symbolcorresponds in a graphical representation to a MICR symbol waveform.Also this method may comprise (d) through operation of the at least oneprocessor, for each MICR symbol waveform, generating data values thatcorrespond to minimum and maximum peaks in the MICR symbol waveform anddetermining a set of amplitude values representative of the amplitudesof the identified peaks. Further this described method comprises (e)through operation of the at least one processor, for each MICR symbolwaveform, comparing the set of amplitude values determined in (d) forthe respective MICR symbol waveform to each one of a plurality ofpredetermined sets of amplitude values for MICR symbols of at least oneMICR font. In addition this method comprises (f) through operation ofthe at least one processor, for each MICR symbol waveform, determiningwhich MICR symbol of the at least one MICR font corresponds to therespective MICR symbol waveform responsive to (e). Also this describedmethod comprises (g) through operation of the at least one processor,determining at least one number (i.e., symbol) from the MICR symbolsdetermined in (f); and (h) through operation of the at least oneprocessor, sending at least one message to a remote computer. The atleast one message includes data representative of the at least onenumber determined in (g).

Of course these approaches are exemplary.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an example isometric view of an example automated bankingmachine.

FIG. 2 is an example schematic view of components included within anautomated banking machine of the type shown in FIG. 1 and a system inwhich the automated banking machine is used.

FIG. 3 is an example schematic view of exemplary software componentsused in connection with the automated banking machine shown in FIG. 2.

FIG. 4 is an example side view of a deposit accepting apparatus used inconnection with an example embodiment.

FIG. 5 is an example schematic view of the deposit accepting apparatusshown in FIG. 4.

FIG. 6 is an example side view of the deposit holding module of thetransport apparatus shown in a position accepting a sheet into a sheetholding compartment.

FIG. 7 is an example schematic view of hardware and software componentsused in connection with the automated banking machine of some exampleembodiments.

FIG. 8 is an example schematic view of the interaction of componentsused in connection with accepting documents in an exemplary depositaccepting system.

FIGS. 9-10 are example schematic views representing a series of stepsexecuted through use of the deposit accepting apparatus in connectionwith accepting a check in the machine.

FIG. 11 is an example schematic view showing how data representative ofan image of a deposited instrument is modified and aligned in an exampleembodiment for purposes of analysis.

FIG. 12 is an example schematic view of the application of a templatefor a particular type of deposited instrument to image data for aninstrument deposited to the deposit accepting apparatus of an exampleembodiment.

FIG. 13 is an example schematic view of an alternative system of anexample embodiment including check accepting automated banking machines.

FIG. 14 is an example schematic view of an alternative system forprocessing check transaction data and image data related to checksreceived through automated banking machines.

FIG. 15 shows an example of a check which is shown divided into fourzones.

FIG. 16 shows an example visual representation of a magnetic image mapfor an ANSI compliant check shown in FIG. 15.

FIG. 17 shows an example visual representation of a magnetic image mapfor a photocopy of a check made without magnetic toner.

FIG. 18 shows an example visual representation of a magnetic image mapfor a magnetic photocopy of a check.

FIG. 19 shows an example embodiment of a magnetic sensor for the depositaccepting apparatus.

FIG. 20 shows an example method of dividing a check into six zones(zones 1-6).

FIG. 21 shows an example of scanning paths for the ten differentmagnetic sensing elements or channels of the example magnetic sensorsuperimposed on a check

FIG. 22 shows an example of a table used to classify a check as eithergood or a potential fraudulent copy.

FIG. 23 shows an example of the U.S. standard MICR E-13B font symbolsand their corresponding electrical signal magnetic waveforms.

FIG. 24 shows an example of a table of the MICR E-13b symbols (columnlabels) and their corresponding determined peak features which comprisetheir respective feature vector.

FIG. 25 shows a portion of a graphical representation of an exampledetected waveform for a detected MICR symbol with two peaks.

FIG. 26 shows shaded areas in a waveform which correspond to calculatedareas or weights determined for the peaks in the waveform.

FIG. 27 shows shaded areas in a waveform which correspond to calculatedcut areas or modified weights determined for the peaks in the waveform.

FIG. 28 shows an example of the MICR zone of a check.

FIG. 29 shows an example of a three dimensional graphical representationof magnetic patterns on a check.

FIG. 30 shows an example magnetic MICR waveform with eight samplelocations.

FIG. 31 shows an example standard feature vector matrix for the CMC-7MICR font.

FIG. 32 shows an example cut of a peak.

FIG. 33 shows an example magnetic MICR waveform with 7 positive peaks.

FIG. 34 shows steps of an example method to determine what MICR symbolis represented in a magnetic waveform using feature vectors.

FIG. 35 shows steps in an example method to determine what MICR symbolis represented in a magnetic waveform using peak weights.

FIG. 36 shows steps in an example method to determine what MICR symbolis represented in a magnetic waveform using anchor depth, weight, cut,and cut series to determine valid peaks.

FIG. 37 shows a graphical representation of an example magnetic waveformafter offset correction and high frequency noise filtering.

FIG. 38 shows steps in an example standard cross correlation matrix forthe E-13b MICR font.

FIG. 39 shows an example method to determine what MICR symbol isrepresented in a magnetic waveform using baseline correction andfiltering.

FIGS. 40-43 show example representations of a document in four possibleorientations.

FIG. 44 shows steps in an example method to detect MICR symbols in anypossible document orientation.

FIG. 45 shows steps in an example method to detect MICR symbols in anypossible document orientation with optical assistance.

FIG. 46 shows an example method to detect MICR symbols using magneticwaveform peaks.

FIG. 47 shows steps in an example method to detect MICR symbols usingmagnetic waveform peak distances.

FIG. 48 shows a visual representation of an example of how to detectMICR symbols using optical scan lines.

FIG. 49 shows an example of graphical representations of optical imageintensity waveforms.

DETAILED DESCRIPTIONS OF EXEMPLARY EMBODIMENTS

Referring now to the drawings and particularly to FIG. 1 and referringto U.S. Pat. Nos. 7,314,163; 7,389,914; 7,467,744; 7,469,824 and6,554,185, which are hereby incorporated herein by reference in theirentirety, there is shown therein an example embodiment of an automatedbanking machine 10 which includes an example deposit accepting apparatusand which performs at least one operation. Automated banking machine 10is an ATM. However it should be understood that the concepts disclosedherein may be used in connection with various types of automated bankingmachines and devices of other types. Automated banking machine 10includes a user interface generally indicated 12. User interface 12includes input and output devices. In the example embodiment the inputdevices include a plurality of function buttons 14 through which a usermay provide inputs to the machine. The example input devices furtherinclude a keypad 16 through which a user may provide numeric or otherinputs. A further input device in this example embodiment includes acard reader schematically indicated 18. Card reader 18 may be of thetype used for reading data bearing records, such as magnetic stripecards, smart cards, RFID tokens or other articles presented by a user.The card data corresponds to a financial account associated with themachine user. Another input device on the example machine includes animage capture device 20. The image capture device may be a camera orother device for capturing the image of a user or the surroundings ofthe machine. The example embodiment may include biometric readingdevices. Such devices may include an imaging or reading device such as afingerprint reader, iris scan device, retina scan device or otherbiometric input. It should be understood that the camera mentioned mayserve as a biometric reading device in some embodiments.

The exemplary user interface 12 also includes output devices. In theexample embodiment shown in FIG. 1 the output devices include a display22. Display 22 includes a visual output device such as a CRT or LCD forproviding messages and prompts to a user. These messages and prompts maybe responded to by inputs from the user through the function buttons 14adjacent to the display or by inputs through the keypad 16 or throughother inputs. A further output device in the example embodiment includesan audio output device schematically indicated 24. The audio outputdevice may be used to provide audible outputs to the user. A furtheroutput device in the example embodiment includes a printer. The printermay be used to provide outputs in the form of receipts or other items orinformation to the user. The printer is in connection with a printeroutlet in the user interface indicated 26 in FIG. 1.

It should be understood that the input and output devices shown areexamples and in other embodiments other types of input and outputdevices may be used. Such input and output devices commonly receiveinformation which is usable to identify the customer and/or theiraccounts. Such devices are also operative to provide information to auser and to receive instructions from a user concerning transactionswhich are to be carried out through use of the machine. Various forms ofuser interfaces and input and output devices may be used in connectionwith various embodiments.

In the described example embodiment ATM 10 includes a cash dispensingmechanism which is alternatively referred to herein as a cash dispenser.The cash dispensing mechanism is selectively operated to enable thedispensing of cash to authorized users of the machine. Cash is providedto the users through a cash outlet indicated 28. A further feature ofthe example embodiment is the ability to accept deposits through theATM. The machine includes a deposit accepting opening 30. In the exampleembodiment the ATM is enabled to accept deposits in the form of sheets,envelopes and other items as later discussed. In some embodiments theATM may have structural components like those shown in U.S. Pat. No.6,010,065 the disclosure of which is hereby incorporated herein byreference in its entirety.

FIG. 2 shows a schematic view of exemplary computer architectureassociated with ATM 10 and a first example system in which it is used.The ATM includes one or more computers therein, which computer orcomputers are alternatively referred to herein as a processor orprocessors. For purposes of this disclosure, a computer or a processorwill be deemed to include a single computer or processor as well asmultiple computers or processors. The one or more computers in theexample embodiment are schematically represented by a terminal processor32. The terminal processor is in operative connection with one or moredata stores schematically represented 34. The terminal processor maycomprise one or more computers that operate to control transactionfunction devices 36 which are included in the ATM. These transactionfunction devices include devices which operate in the ATM to carry outtransactions. Transaction function devices may include, for example,currency dispensing mechanisms, currency presenters, currency acceptors,currency validators, item dispensing devices, card readers, printers,depositories, other input and output devices and other devices.Transaction function devices may further include cameras, sensors, imagecapture devices and other items. Transaction function devices may alsoinclude one or more processors. The particular symbol of the transactionfunction devices depends on the particular capabilities for carrying outtransactions to be provided by the ATM.

In the example embodiment ATM 10 exchanges messages through acommunication interface 38 with a communications network 40. Network 40may be one or more types of data communications networks, including aphone line, data line, lease line, frame relay, wireless network,telecommunications network, local area network, wide area network orother medium for communicating messages to and from the ATM 10. Thecommunications interface provided is suitable to work in connection withthe particular type of network(s) to which the machine is connected. Inthe example embodiment the ATM may be connected to a network whichcommunicates with a plurality of ATMs such as Cirrus® or Plus®, or otherdebit card network. Of course in other embodiments other suitablenetworks for processing credit, debit or other types of onlinetransactions may be used including the Internet. Exemplary systems mayalso include features described in U.S. patent application Ser. No.10/980,209 filed Nov. 2, 2004 the disclosure of which is incorporatedherein by reference in its entirety.

As schematically represented in FIG. 2, network 40 may be in operativeconnection with one or more host computers 42, also referred to hereinas a banking host. Host computers 42 in the example embodiment areoperative to authorize transaction requests which are made by users atthe ATM 10. The card reader in the ATM is operative to read data fromuser cards. The data corresponds to at least one of a user and afinancial account. The ATM is also operative to receive from the user apersonal identification number (PIN) that is input by a user through akeypad. The ATM is operative to deliver to the host computer, dataidentifying the user and/or their account, data corresponding to thePIN, and the particular transactions that they wish to conduct. Therequest is routed through the network to a host computer that canevaluate and/or authorize the request. The exemplary host computer isoperative to authorize or deny the transaction based on a comparison ofdata that corresponds to the data read from the user card and storeddata corresponding to authorized users and their associated PINs. Theappropriate host computer receives and analyzes the received data andreturns to the ATM a message which indicates whether the transactionrequested is authorized to be conducted at the machine. The messagereturned may also include one or more instructions that cause the ATM tocarry out one or more transaction functions. In response to receiving amessage indicating that the transaction should proceed, the processor inthe ATM operates the transaction function devices to carry out therequested transaction. If the transaction is not authorized, the user isso informed through the display or other output device and thetransaction is prevented. The ATM is also operative in the exampleembodiment to send to the host computer authorizing the transaction, acompletion message which includes data indicative of whether thetransaction was able to be carried out successfully. Upon receiving theinformation that the transaction was carried out, the host computer isoperative to take appropriate action such as to credit or debit a user'saccount. It should be understood that this system shown in FIG. 2 is anexample and in other embodiments other approaches to operating automatedbanking machines and authorizing transactions may be used.

In the described example embodiment the transaction function devicesinclude a check acceptor which comprises a deposit accepting apparatus.The example deposit accepting apparatus is capable of acceptingdeposited items such as envelopes as well as sheets and documents suchas checks. This deposit accepting apparatus in alternative embodimentsmay be capable of accepting and analyzing other items such as papers,instruments, billing statements, invoices, vouchers, wagering slips,receipts, scrip, payment documents, driver's licenses, cards and itemswhich may be moved in the deposit accepting device. Alternativeembodiments of a deposit accepting apparatus may accept only selectedones of deposit items. The example deposit accepting apparatus mayalternatively be referred to herein as an “intelligent depositorymodule,” “depository module” or “IDM.” An example embodiment of the IDM44 is shown in FIG. 3 and example mechanical components thereof areshown in FIGS. 4-6. It should be understood that for purposes of thisapplication, a deposit accepting apparatus or deposit acceptorencompasses any mechanism that accepts an item into an automated bankingmachine.

As shown in FIG. 4, IDM 44 includes a transport section 46. Transportsection 46 extends in generally a straight path from an inlet 48 to anoutlet 50. Inlet 48 is positioned adjacent to a deposit acceptingopening 30 through the body of the ATM 10. Access to the transportsection 46 from the outside of the ATM may be controlled by a gate 52 orother suitable blocking mechanism which operates under the control ofthe terminal processor 32. The terminal processor operates to open thegate only when an authorized user of the ATM is to provide items to orto receive items from the transport section of the IDM. The IDM 44 maybe like those shown in U.S. patent application Ser. No. 11/371,372, U.S.patent application Ser. No. 11/983,401 filed Nov. 8, 2007 and/or U.S.Pat. No. 7,448,536, the disclosures of each of which is herebyincorporated herein by reference.

The transport section 46 of the exemplary IDM includes a plurality ofbelts or other moving members 54. Moving members 54 operate to engageitems deposited into the transport section and to move deposited itemsin engagement therewith. The moving members are moved in response to oneor more drives schematically indicated 56. In this example embodiment aninlet transport section 58 moves deposited items between upper and lowerbelt flights (see FIG. 5). Similarly, deposited items are also movedthrough an outlet transport section 60 in sandwiched relation betweenupper and lower belt flights. Between the inlet and outlet transportsections deposited items are moved past an analysis module 62. In thisexample embodiment deposited items are moved adjacent to the analysismodule in engagement with moving members that act on the lower side ofthe deposited item. In this way the deposited item moves in closeproximity to the analysis module and in sandwiched relation between alower face 64 of the analysis module and the upper face of the movingmembers. Of course it should be understood that this configuration is anexample. In other embodiments additional analysis modules may beprovided so that both sides of an item are analyzed. Analysis modules ordiscrete devices for activating indicia to facilitate sensing, as wellas for sensing indicia on items, may be provided as necessary to readindicia from items handled by the banking machine.

The example embodiment further includes a deposit holding moduleschematically indicated 90 (see FIG. 4). In the example embodiment thedeposit holding module includes a plurality of compartments which aremoved relative to the outlet 50 of the transport section to enable itemsto be passed from the transport section into a selected compartment. Thedeposit holding module includes a drive which is part of a translationmechanism 94 of the screw type. The translation mechanism operates tomove the compartments in a generally vertical direction relative to theoutlet 50 in the transport section. The deposit holding module furtherincludes a tamping member 96 which is movable in the compartment andoperates to tamp sheets held in a sheet holding compartment so as toreduce the volume of sheets held therein until the items may be removed.

In the example embodiment the analysis module 62 includes opticalscanning sensors schematically indicated 132 in FIG. 5. The analysismodule may serve as a check imaging device. Scanning sensors 132 areoperative to generate an image of documents that move adjacent to theanalysis module. In the example embodiment the scanning sensors scangenerally the entire transverse path through which documents may travelin the transport section. The scanner in the described embodimentgenerates radiation in the visible range and resolves images atapproximately 240 dots per lineal inch. The scanning sensor is alsooperative to have a focal length which corresponds to the distance thatthe scanned documents are disposed from the surface of the sensor asthey pass the analysis module. In the example embodiment the scanningsensor 132 has a focal length of about 4 millimeters. Of course in otherembodiments other types of scanning sensors may be used. Such othertypes of sensors may include emitters and sensors for sensing radiationat discrete frequencies in the visible or non-visible range. In additionmultiple sensor types may be used on one or both sides of documents.Various types of sensors may be used having different scanningresolution. The imaging device of the example embodiment is operativeresponsive to an associated processor to produce image data, whichcomprises electronic data which corresponds to a full or partial visualimage corresponding to the visual appearance of the scanned check orother item.

The example analysis module further includes a magnetic sensor includingsensing elements 134. The magnetic sensing elements 134 are operative tosense the magnetic properties of documents which pass adjacent to theanalysis module. In the example embodiment the magnetic sensing elements134 include a plurality of discrete transversely spaced magneticsensors. The magnetic sensors generally each cover a relatively smallportion of the overall transport width. The sensors are arranged insufficient proximity so that substantially the entire transverse widthof the document path is sensed. The analysis module further includes amagnet 136. Magnet 136 may comprise a unitary or a plurality ofpermanent or temporary magnets. In the example embodiment permanentmagnets are used. The permanent magnets operate to activate magneticproperties of magnetic inks on documents passing adjacent to theanalysis module. These magnetic properties may then be more readilysensed by the magnetic sensing elements 134.

It should be understood that the particular sensors and devices inanalysis module 62 are examples. Other embodiments may include only anoptical scanner or magnetic sensing elements, or different or additionaltypes of scanning and sensing elements. For example, some embodimentsmay include scanners for reading bar code or other types of opticalindicia. Other embodiments may include devices for reading magnetic fluxreversals that may be encoded in a magnetic media. Some embodiments mayinclude read heads for reading MICR symbols or other magneticallysensible features. Other embodiments may include devices which areoperative to detect the presence of holograms or to read non-visibleradiation, fluorescent inks, or other types of coding. The particularactivating and sensing devices included in a particular analysis modulewill depend on the particular types of documents to be verified andanalyzed through operation of the embodiment.

FIG. 3 shows schematically the relationship of the IDM 44 with examplesoftware components which operate in the terminal processor 32. Theterminal processor 32 has operating therein an operating system layerschematically indicated 138. The operating system layer 138 may includeoperating systems such as OS/2® from IBM, Windows NT® or Windows XP®from Microsoft, Linux or other suitable operating system. The operatingsystem communicates with a terminal control software layer 140. Theterminal control layer in the example embodiment operates to controlnumerous aspects of the ATM functions including aspects of thetransaction function devices. As schematically represented in FIG. 3 theterminal control software sends messages to and receives messages fromdevices associated with the IDM 44. The messages are generally operativeto control mechanical components of the IDM as well as to receive inputsfrom sensors and other devices which operate in connection with thedeposit accepting function.

The example software architecture also includes a recognition subsystemsoftware layer 142. The recognition subsystem layer also communicateswith the operating system layer and the terminal control software layerto control and receive inputs from the IDM. The recognition subsystemlayer includes software which functions to control, manipulate andanalyze image data received from the IDM as schematically represented byimage control component 144. Another software component of the examplerecognition subsystem layer accomplishes symbol recognition. This symbolrecognition component schematically represented 146 in the exampleembodiment is operative to identify MICR coding and numerical symbols.In the example embodiment the symbol recognition software includessoftware that is commercially available from Carreker Corp. Otherproviders of symbol recognition software include Parascript, Mitek andA2iA. Of course other suitable recognition software may be used. Therecognition subsystem 142 of the example embodiment also includes amagnetic data control component schematically represented 145 that isoperative to analyze and to manipulate data received from the magneticsensing elements and to check for correlation between the magnetic datathat is sensed and the optical data which is obtained from the scanningactivity. Of course these software functions are examples and thesefunctions may be programmed differently and other or additional softwarecomponents may be included in other embodiments.

FIG. 7 shows the example schematic components of the software in greaterdetail. As can be appreciated the operating system 138 in the terminalprocessor is in operative connection with one or more data stores 34.The data store may include the data corresponding to informationconcerning programs, transactions, instructions and other data orprogram logic which are necessary to control the operation of the ATM.In addition the data store includes the data used in connection withanalyzing and verifying documents. As later discussed the data store mayalso include image data corresponding to the images of documents thathave been accepted by the system as well as transaction identifyingdata. The software in connection with the example terminal processoralso includes a communication subsystem layer 148. The communicationsubsystem layer enables communication between the various softwarecomponents of the system. The communication subsystem layer alsocommunicates with the various transaction function devices 36 throughappropriate interfaces or drivers. In addition communication layer 148in the example embodiment also enables communication through appropriateinterfaces 38 to one or more communications networks 40 and the hostcomputers 42 which are operatively connected thereto. Of course thissoftware architecture is merely an example and in other embodimentsother approaches may be used.

In the example embodiment the IDM 44 includes an onboard computerprocessor which resides on a scanner card 150. The scanner card 150further receives and operates upon data from the optical scanningsensors 132 on the analysis module 62. The scanner card further hasincluded thereon a driver schematically indicated 152. The driver isoperative to communicate through a scanner interface 154 with theoperating system 138 and the data store 134. The driver 152 is alsooperative to control the scanning activity which is carried out by thescanner card 150. In the example embodiment the driver is also operativeto control the allocation of memory for use in the scanner operation.This assures that adequate memory is available in RAM to carry out thecapture, storage and analysis of the scanning data as required toanalyze and authenticate documents which may be input in the machine.

As represented in FIG. 7 in the example embodiment, when a document isto be scanned the terminal control software 140 causes the particulardocument to be moved as desired in the IDM 44. This is done bycontrolling the various devices which sense and move documents in andthrough the module. The terminal control software 140 operates inconjunction with the recognition subsystem 142 which providesinstructions to the scanner card 150 to scan documents using the opticalscanning sensors 132 during the appropriate time periods. The data fromthe scanning process and magnetic sensing operations is returned throughthe operating system to memory. The data is then recovered from memoryand manipulated responsive to the image control and symbol recognitionfeatures of the recognition subsystem 142. The results of themanipulation and analysis of the scanned data is then communicatedthrough the terminal control layer to a remote host 42. This is done inthis example embodiment using transaction request and authorizationmessages of a type that can be handled within the framework of ATMtransaction processing systems.

In some example embodiments the ATM operates to receive identifying datafrom the user in the manner previously discussed. The user identifiesthe particular transaction type to be associated with the transaction.In this case the user may indicate that the user has selected the optionof cashing a particular check. Next the user provides inputscorresponding to the amount associated with the transaction they wish toconduct. In response to these inputs the terminal processor may operatein accordance with its programming to open the gate 52 adjacent theopening to the transport section 46 of the IDM 44. The user may nowinsert a check into the opening. The document is then moved past theoptical and magnetic sensors in the analysis module 62 as represented inFIG. 5. As the document moves past the analysis module, the terminalcontrol software and recognition subsystem software gather the image andprofile data that is used to analyze and/or produce an electronic imageof the document. As the check 158 passes the magnet 136 the magnetic inkthereon is magnetized. This magnetized ink is then sensed by themagnetic sensors 134 which provide a profile of the area in whichmagnetic ink is present.

As also represented in FIG. 5 movement of the document past the scanningsensors 132 causes image data to be produced which is indicative of theoptical characteristics of the document passing in the transportsection. This image data corresponds to an electronic image of the checkthat is captured through operation of the scanner card and included inthe data store associated with the ATM. The scanning process iscontinued as the check 158 moves past the analysis module 162 as shownin FIG. 4.

The terminal processor next operates to apply the rules which areimplemented through operation of the programs stored in memoryconcerning the particular type of document associated with thetransaction. Generally at least one input by the customer indicatingthat they are making a check deposit may be correlated with certainstored data or rules which indicate the particular characteristics ofthe document that is to be received. In some cases the inputs maycorrespond to a particular sized document. Alternatively the rules maycorrespond to particular configurations or other characteristics. Inthis example the rules stored in memory are also indicative of “windows”or particular zones or areas in the document landscape in which datawhich should be analyzed on the document may be found.

In accordance with the example embodiment which operates to analyzecheck 158, the terminal processor operates in accordance with theapplicable rules recovered from memory as associated with a checkdeposit to deskew the data corresponding to the image and place it inregistration with an imposed coordinate system. This is done in theexample embodiment through use of a programmed series of steps whichfinds the boundaries of the image data. This is done by comparing thepixels which make up the image and generating at least two of the lineswhich bound the document. By identifying these lines, one or morecorners of the document may be identified. This process is representedin FIG. 11 by the skewed profile of check 158 which is shown in solidlines.

In the example embodiment, after finding the two leading corners of thedocument 166 and 168 and the most closely adjacent trailing corner to anX coordinate 170, the terminal processor operates in accordance with itsprogramming to adjust the data corresponding to the image. The exampleterminal processor first operates to adjust the image by rotating theimage data about corner 168. This causes the image to be “squared up”relative to the imposed coordinate system as represented by a phantomimage 172. The computer next operates to shift the squared up image datato a reference point of the coordinate system. This shifting places theleading corner 168 at the origin of the imposed X and Y coordinatesystem. The leading corner 166 is placed along the Y axis while thetrailing corner 170 is placed along the X axis. It should be understoodthat all of the pixels which make up the image data are correspondinglyadjusted through this process to produce the shifted image 174 which isshown in phantom in FIG. 11.

The terminal processor next operates in accordance with its programmingto apply template logic to the shifted image 174. The computer operatesto recover from memory, data corresponding to at least one selectedtemplate. In example embodiments a plurality of templates may be storedin memory and the selected one is recovered responsive to customerinputs to the machine, indicia read from the document or other data. Inthis step the computer operates to apply a template over the shiftedimage to identify for analysis “windows” within the image that containsdata that is of interest. This is represented schematically in FIG. 12.In FIG. 12 a template is schematically indicated 176. Template 176includes a first window 178 which generally corresponds to a zone or anarea in which a MICR line on a check may be located. Template 176further includes a second window 180. Window 180 corresponds to a zoneor an area of the landscape on the check where a courtesy amount whichrepresents the value of the deposited check may be located. It should beunderstood that these windows are examples and in other embodimentsother or additional windows may be included. Such windows may include,for example, a window for the so called legal amount which is thewritten or typed amount of the check. A window may also be provided foran “amount not to exceed” indicator, date, payee name, payor name orother information that appears on the check. It should further beunderstood that these processes for identifying windowed zones or areaswithin shifted data are carried out through operation of the at leastone processor and the computer executable instructions included in therecognition subsystem software, and that these graphic representationsshown in the Figures merely serve to explain the nature of an exampleform of the analysis that is carried out.

The computer may operate to analyze the data in the window of thetemplate which corresponds to the potential location of the MICR line.This is accomplished by the image control component 144 of the softwareanalyzing data from the data store. It should be understood that thedata within the particular window may or may not correspond to the MICRline depending on the orientation of the document as well as whether thedocument itself is valid.

The computer may then operate to pass the data extracted from the window178. This symbol recognition software component is operative to applythe logic used for optically reading MICR symbols. Any method operativeto read or detect the MICR symbol may be used. U.S. Pat. Nos. 5,303,311and 5,105,470 describe some example embodiments of optically recognizingsymbols and the disclosures of each of them are hereby incorporatedherein by reference. In the example embodiment this logic may beassociated with reading E-13B or CMC-7 type symbols. The symbolrecognition software component 146 is operative to analyze the data andmake evaluations in looking for known symbols of the particular type. Inthe example embodiment the symbols represented which are resolved areprocessed to derive ASCII values corresponding to the symbols.

The recognition subsystem software 142 may next operate to determine ifthe degree of assurance or confidence as indicated by the symbolrecognition component for the values returned, is above a threshold. Thedetermination of the level of assurance is based on one or more valuesdelivered by the pattern recognition algorithms in the symbolrecognition software component used in the example embodiment. Thecomputer may operate in response to its programming to proceed based onwhether the level of assurance is at or above, or below the threshold.Of course this approach is an example and in other embodiments otherapproaches may be used.

If the level of assurance in the determined MICR values is indicated asbelow the threshold and/or if routing and transfer symbols are notfound, the recognition subsystem through operation of the image controlsoftware component, operates to further manipulate the image. In theexample transaction the computer operates to manipulate the data toessentially transpose and flip the image 180 degrees and to again readthe data in the MICR line window. It should be understood that in otherembodiments the data corresponding to the image may be manipulated inother ways in order to attempt to translate the image so as to findappropriate data.

The translated image data now in the window 178 may again be read andpassed to the symbol recognition software component 146. This againcauses the output of ASCII values based on the symbols in the window. Ifthe MICR values read have an associated level of assurance at or abovethe threshold and routing and transfer symbols are present, therecognition subsystem is operative to proceed with further analysis ofthe image. However if the level of assurance remains below the thresholdand/or there are no routing or transfer symbols, this may be anindication that the document is not valid. In some embodiments the ATMmay operate to further transpose the data and conduct additionalanalysis. This may be particularly appropriate in situations where bothsides of the document are being scanned and the document may be indifferent orientations. In this case the terminal processor may causethe ATM to operate to return the document to the customer and to closethe transaction.

The example embodiment has a recognition software subsystem that furtheroperates to check for the presence of magnetic ink on the document inthe proper location. This is done in the example embodiments bycomponent 145 determining the length and configuration of the magneticprofile associated with the document. This length and orientation datamay be normalized in the manner of the image data based on the imposedcoordinate system, and compared therewith to verify that the magneticareas correspond to the optical data corresponding symbols in the MICRline.

It should be understood that while the example embodiment has beendescribed as reading checks and vouchers, other embodiments may be usedfor reading other document types. Such other document types may includefor example statements of charges such as deposit slips, utility bills,credit card bills and other statements of charges. Embodiments mayfurther be adapted to read other or additional types of coding such asone or two-dimensional bar codes, other symbol sets, alphabets ofvarious languages or other symbols. Embodiments may accept only one typeof item, or a plurality of types of items. Further, while the exampleembodiment accepts envelopes, other embodiments may not accept suchitems, or may accept other types of items.

It should be understood that the architecture of the computers andsoftware described is an example. Other embodiments may use differentcomputer and/or software architectures to accomplish the functions andmethods described. Further the one or more computers operating in anautomated banking machine may be programmed by reading through operationof one or more appropriate reading devices, machine readable articleswhich comprise media with computer executable instructions that areoperative to cause the one or more computers (alternatively referred toherein as processors) in the machine to carry out one or more of thefunctions and method steps described. Such machine readable media mayinclude for example one or more CDs, DVDs, magnetic discs, opticaldisks, flash memory, tapes, hard disk drives, PROMS, memory cards orother suitable types of media.

FIG. 13 shows an alternative example embodiment of a system generallyindicated 200, in which check cashing is provided through automatedbanking machines. The system includes automated banking machines 202which may be automated teller machines of the type previously discussed.ATMs 202 are connected through a network 204, to a host computer whichis alternatively referred to as a transaction server generally indicated206. Network 204 may comprise any of a number of public or privatenetworks suitable for communicating between host computer 206 and theATMs. As schematically represented in FIG. 13, host computer 206 is inoperative connection with at least one data store 208 which includesvarious types of instructions and stored data. Host 206 is also inoperative connection with a host interface terminal 210. As can beappreciated, data stores are also referred to herein as computermemories.

In the example embodiment system 200 includes at least one administratorstation 212. Administrator station 212 in the example embodiment is acomputer or server in operative connection with the network 204.Administrator station 212 is used by the operator of the ATMs 202 forpurposes of configuring the system and monitoring transactions whichoccur at the ATMs 202.

Example system 200 further includes a check image server 214. As shownschematically, the check image server 214 is in operative connectionwith a data store 216. The check image server 214 comprises a computerthat is connected to ATMs 202 through a network 218. Network 218 may bethe same or different network than network 204. Other servers 220 and222 are connected to the network 218. In the example embodiment thecheck image server 214 is operative to receive data corresponding toelectronic images of checks that are received at the ATMs 202. The checkimage server 214 may be used to archive data corresponding to suchimages and to accomplish settlement among the various entities whichhold accounts which must be credited and debited in the conduct of acheck cashing transaction.

In the example embodiment of system 200, ATMs 202 are specificallyoperated for purposes of providing check cashing services. Such checkcashing services may be provided for persons holding accounts with theoperator of the system such as a financial institution. Alternatively insome embodiments ATMs 202 may be specifically operated to provide checkcashing services for persons who do not hold accounts with the operatorof the system but who have a need to cash checks drawn by makers whohave accounts or other relationships with the operator of the system.This may be, for example, a situation where a particular entity hascontracted with the operator of the system to honor checks for which theentity is a maker and which are deposited in a machine. Otherembodiments may be operative to cash checks for which the particularmaker of the check has an account relationship with the operator of thesystem. As later discussed, in some example embodiments checks may becashed at the ATMs 202 by users who are associated with the makers ofchecks and who are correlated with data corresponding to such makers inone or more data stores operatively connected to the system.

An alternative embodiment of a system for cashing checks through ATMsand delivering images of such checks for further processing isrepresented by a system generally indicated 350 in FIG. 14. System 350includes a plurality of ATMs 352 which communicate through one or morenetworks 354 with one or more remote computers represented as an ATMhost 356. ATM host 356 communicates with the ATMs to conducttransactions generally in the manner previously described. In theexample embodiment the ATM transaction host can communicate with theATMs 352 for purposes of carrying out a plurality of transactions. Thesemay include cash dispensing transactions that do not involve receipt ofa check, deposit accepting transactions which involve receipt of deposititems such as checks, balance inquiries, account transfers and/or othertransactions depending on the ATM type used.

The example system 350 differs from the systems previously described inthat image data corresponding to electronic images of both the front andthe back of each check presented at the machine is delivered remotelyfrom the machine for purposes of further processing. Further processingis facilitated in the example embodiment by the ATM providing image datawith transaction identifying data which can be used to facilitate thefurther processing of the transaction. In the example embodiment thetransaction identifying data is provided by the ATM host in the messagethat the host sends to the ATM authorizing the acceptance of the check.This transaction identifying data may include the information that isneeded for further processing of a settlement of the check. In someembodiments this enables the image messages which are delivered by theATM, to be used to process the check electronically as a substitute forthe paper document. This may also avoid the need to recover someadditional transaction data from other sources or systems because suchdata has been associated by the ATM with the image as part of the imagemessage. Of course this approach is an example and in other embodimentsother approaches may be used.

As discussed previously and as discussed in U.S. Pat. No. 6,554,185 andU.S. Application No. 60/584,622 filed Jun. 20, 2004, which are herebyincorporated herein by reference in their entirety, example embodimentsof an IDM may include an analysis module with magnetic sensing elementscapable of detecting magnetic properties of checks. The presence orabsence of magnetic features in different areas of the check may beevaluated to determine whether the check is authentic or a fraudulentcopy. In an example embodiment, movement of the check across themagnetic sensor of the analysis module is operative to generate datacorresponding to a magnetic image map of the magnetic ink printed on thecheck. Signals generated by the sensor which are electrical signalsrepresentative of the presence of magnetic material may be processed toderive data corresponding to a two-dimensional array of pixels, whereeach pixel represents a level or strength of magnetic material for theparticular area on the check for which the pixel was measured.

Different areas or zones of the magnetic image of the check may beevaluated by one or more processors for the presence or absence ofmagnetic ink based on the values of the pixels in the magnetic image.For example the image map of a check may be partitioned into a pluralityof zones. FIG. 15 shows an example of a check 500 which is shown dividedup into four zones (indicated with dashed lines). These zones includethe previously described “MICR” zone 502, a leading blank zone 504, atrailing blank zone 506, and a background zone 508.

In an example embodiment, these zones may vary in location depending onthe size and the orientation of the check as the check passes across themagnetic sensor. Therefore, as discussed previously, the optical imagescan captured by the optical sensors of the IDM may be evaluated todetermine the corresponding areas of the magnetic scan which correspondto these four zones. Also in alternative example embodiments additionalzones may be evaluated including zones associated with different areasof the background zone including zones corresponding to the payeeinformation, payee bank information, payor information, legal amount,courtesy amount, check number, signature line and memo field.

FIG. 16 shows a visual representation of data corresponding to amagnetic image map 520 for an ANSI compliant check 500 shown in FIG. 15and scanned with an example embodiment of the IDM. Here the gray areas522-530 represent the presence of magnetic ink on the check, with thedarker areas representing a stronger magnetic flux intensity. The whiteareas represent areas of the check in which the measured magneticintensity is below a threshold value.

In this example, the MICR line 528 is represented as a gray band at thebottom of the magnetic image map. The other gray areas 522, 524, 526,530 correspond to text on the check which is printed with magnetic inkin the background zone. For example, the gray area associated withreference numeral 522 in FIG. 16 corresponds to the printed name andaddress of the payor which is also depicted with reference numeral 542in FIG. 15.

One possible method to produce a fraudulent check is to photocopy thecheck with a standard photocopier which does not include magnetic toner.The resulting copy may optically look like the original check 500 shownin FIG. 15. However, the ANSI standard requires the MICR line to beprinted with magnetic ink or toner. Thus a magnetic image map of such aphotocopy as produced by an example embodiment of the IDM will show theabsence of magnetic ink or other material on the check. FIG. 17 shows avisual representation 550 of a magnetic image map for a photocopy of acheck made without magnetic ink or toner. Here the visual representationof the magnetic image map lacks the gray areas shown in thecorresponding visual representation of the magnetic image map 520 inFIG. 15 for an original or authentic check. Example embodiments of theprocessor of the IDM and/or ATM are operative to evaluate the datacorresponding to a magnetic image map acquired by the IDM for aphotocopy of a check. Based on the absence of magnetic material in theMICR zone 552 (FIG. 17), the processor may be operative to classify thecheck as being a possible forgery for which the check may be returned tothe user, confiscated, marked and/or flagged as being suspect.

Another possible method to produce a fraudulent check is to photocopythe check with a photocopier which includes magnetic toner. Theresulting photocopy may optically look like the original check 500 shownin FIG. 15. However, all of the text, graphics or other indicia on thecheck may be magnetic. FIG. 18 shows a visual representation 560 of amagnetic image map for a magnetic photocopy of such a check. Here thevisual representation of the magnetic image map includes substantiallymore gray areas compared to visual representation of the magnetic imagemap 520 shown in FIG. 15 for an original or authentic check.

Based on statistics, authentic checks often do not include magneticmaterial in the trailing and leading blank zones 504, 506 (FIG. 15).However, as shown in FIG. 18, a magnetic image map of a magneticphotocopy of this check, may show the presence of magnetic material inthe trailing and leading blank zones. For example, this may be caused bythe cosmetic border 566, shown in FIG. 15 on the original check beingreproduced in the magnetic photocopy with magnetic toner. Referring backto FIG. 18, responsive to the detection of magnetic material in theleading blank zone 562 and/or the trailing blank zone 564, the processormay be operative to classify the check as being a possible forgery forwhich the check may be returned to the user, confiscated, marked and/orflagged as being suspect.

As will be discussed in more detail below, other characteristics such asoptical characteristics of the check may be evaluated through operationof one or more processors in addition to the magnetic image map whenvalidating a check. Also, the example embodiments of the IDM may beconfigurable as to the degree of sensitivity for which checks areevaluated. For example one configurable setting associated with the IDMmay cause the method of classifying checks to be less sensitive byevaluating only a limited number of features or characteristics of thecheck, while a more sensitive configurable setting may cause morefeatures or characteristics of the check to be evaluated. Configurablesensitivity settings enable the owner or operator of the ATM whichincludes the IDM to configure the IDM to their preferred level of riskfor accepting check deposits. For example a less sensitive setting ofthe processing and analysis of the data obtained through the IDM, may bemore likely to accept authentic checks which do not comply with the ANSIstandards or statistically normal checks, at the expense of increasingthe risk that fraudulent checks will be accepted. Whereas, a relativelymore sensitive setting of the processing and analysis of the IDM mayhave a lower risk of accepting fraudulent checks, at the expense ofrejecting a relatively higher percentage of authentic checks.

For example, a relatively less sensitive setting of the IDM, may causethe processor which is in operative connection with the IDM to onlyvalidate whether any magnetic ink is present on the check, while arelatively higher sensitivity setting may validate whether the MICR lineis magnetic. In addition a further relatively higher sensitivity settingassociated with the IDM may cause the processor associated with the IDMto evaluate both the presence of magnetic ink and the absence ofmagnetic ink in one or more zones of the check when determining whetherto reject a check. For example for a check to be determined as valid oracceptable to deposit, the processor of the IDM may validate that themagnetic material is present in the MICR zone, absent from the MICRclear band(s), and absent from the leading and/or trailing blank zonesof the check.

In example embodiments of the IDM, the data acquired from the magneticsensor may need to be processed in order to acquire information whichaccurately reflects the location of magnetic material on the check. Forexample, the physical transport of the check across the magnetic sensormay produce a significant amount of vibration in the check and/ormagnetic sensor. The vibrations may be caused by a motor, a roller,and/or the impact of the check hitting and leaving the sensors in theIDM. Such vibrations may interfere with the ability of the sensor toaccurately produce electrical signals that correspond to datarepresentative of the magnetic properties across the surface of thecheck. In addition, different authentic checks may have magnetic inkprinted thereon which have significantly different levels of magneticflux as measured by the magnetic sensor of the IDM.

In example embodiments, these variations in the magnetic properties ofthe check and the variation in sensor sensitivity caused by thevibration of the check may decrease the accuracy of the analysis carriedout in connection with the IDM unless the data acquired from the sensoris processed appropriately. The following example describes an exampleembodiment for a method of processing the data acquired by the magneticsensor to enable the processor associated with the IDM to moreaccurately evaluate the magnetic image scans of a check.

FIG. 19 shows schematically an example embodiment of a magnetic sensor600. Here the sensor includes a plurality of sensor elements 602arranged in two vertically offset columns. As the IDM transport moves acheck 610 across the magnetic sensor 600, a circuit associated with themagnetic sensor is operative to measure electrical signalsrepresentative of the level of magnetic flux detected by each sensorelement during the time period the check transverses the sensor. Thecircuit which in some embodiments may include one or more processors, isoperative to perform analog/digital conversion of the signals to producea plurality of data sets representative of sensed magnetic levels. Foreach sensor element (e.g. element 620), the data sets correspond torelative levels of magnetic flux for a horizontal band (e.g. band 622)that spans the width of the check from at least the leading edge 612 tothe trailing edge 614 of the check.

In example embodiments, an example magnetic sensor may have ahalf-bridge structure with (strong) permanent magnet backing (bias). Themagnetic sensor may be a differential sensor with an output (voltage)proportional to the magnetic difference under the two magneto-resistivesections from the half-bridge. In an example embodiment the sensor mayhave ten sensor elements (also referred to herein as channels) eachbeing 10 mm wide and covering a total width of 100 mm. Thus along acenterline of the sensor 604 there may be no gap between consecutivesensing channels. As shown in FIG. 19, the odd and even channelsalternate across the centerline. This zigzagged offset may require asoftware correction when piecing together a magnetic image scan and/orperforming noise reduction algorithms on the sensor data. Of course thisstructure is exemplary and in other embodiments other structures andcircuitry may be used.

In some example embodiments, the electrical signals generated by thesensors may be processed through circuitry which includes a processorand which includes an A/D conversion which produces a series of 14,000signal samples for each sensor element as the check passes across thesensor. The resulting signal sample data may be expressed and consideredfor explanatory purposes as a 10 by 14,000 matrix of sensor signal data.This matrix may correspond to a two-dimensional area which is largerthan the two-dimensional surface area of a check. For example, in anexample embodiment the matrix may correspond to an area of about 10 cmby 28 cm (Height by Width). Because a standard sized check may have amuch smaller size (e.g. 7 cm by 19 cm), one or more edges of the matrixmay include data values captured when no portion of the check wasadjacent the sensor.

Because of Op Amp offset (and drift), a baseline correction (or offsetremoval) calculation may be performed in an exemplary embodiment foreach element in the matrix. In this described embodiment, each sensorelement (or channel) may be associated with a different Op Amp and thusa different offset value (n). Thus for each row in the matrix, adifferent offset value associated with that row may be subtracted fromand/or added to each of the 14000 data values in the row. In thisdescribed example embodiment an offset value for each of the ten rowsmay be determined by the associated circuitry calculating the average ormean of all of the data values in the row. In some example embodiments,the matrix after offset removal and/or other calculations may becomprised of non-negative values which range from 0 to 128.

In this described example embodiment, the matrix may be mathematicallymanipulated through operation of the circuitry in a manner that may beconsidered horizontally contracted to generate a relatively smallermatrix with 280 data elements (referred to herein as pixels) in each ofthe ten rows. For example each set of 50 consecutive data elements in agiven row of the matrix may be averaged to produce a value for a pixel.After contraction, the original matrix is reduced from 10 by 1400 dataelements to 10 by 280 pixels.

Given the physical dimensions of the exemplary sensor, the transportspeed of the check and the sample rate of the circuit which acquiresdata values from the sensor elements, in this described exampleembodiment, a pixel may correspond to an area on the check with magneticpresence of 1 by 10 mm² (orientated 10 mm in vertical height and 1 mm inhorizontal length with respect to the check shown in FIG. 15). Forexample, a check with a 72 mm vertical height and 152 mm horizontalwidth may be represented by 7 by 152 pixels. The maximum capacity of thedescribed 10 by 280 pixel matrix may accommodate a check as large as 100mm by 280 mm. However, in other configurations of the IDM, other sizesof the pixels and/or sensor may be used. In this described exampleembodiment, the contractions of the matrix through operation of theassociated circuitry introduces low-pass filtering due to the averagingof the 50 data elements per pixel.

As discussed previously, the mechanical vibration caused by thetransport of the check across the exemplary magnetic sensor mayintroduce considerable noise. However, this vibration generally effectsthe plurality of sensor elements of the magnetic sensor in the samemanner. As a result the vibration waveform which introduces noise intothe sensor element signals is substantially similar for each sensorelement. Therefore as used herein such vibration induced noise presentin each of the 10 rows of the above described magnetic image map matrixis refereed to as a common mode noise.

The true magnetic signals which comprise electrical signalscorresponding to the magnetic ink on the check in an exemplaryembodiment are in general riding on top of the common mode noise andhave a significantly higher amplitude than the noise floor.

An example embodiment of the IDM includes circuitry that is operative totake advantage of these characteristics of the vibration induced noisein the magnetic signals to further process the corrected matrixdescribed above to remove common mode noise. For example the abovedescribed contracted magnetic image map matrix may comprise data thatcan be represented as shown in Equation 1:

$\begin{matrix}{X = \begin{bmatrix}X_{1,1} & X_{1,2} & \ldots & X_{1,280} \\X_{2,1} & X_{2,2} & \ldots & X_{2,280} \\\vdots & \vdots & \; & \vdots \\X_{10,1} & X_{10,2} & \ldots & X_{10,280}\end{bmatrix}} & {{EQ}\mspace{14mu} 1}\end{matrix}$The example embodiment may derive an estimate for a common mode noisefloor (F) from the average of each column vector in the matrix (X). Forexample, the circuitry may operate to store that data in one or moredata stores and a processor of the circuitry may calculate for eachcolumn of the matrix (X) a common mode noise floor value (F) accordingto Equation 2 as follows:

$\begin{matrix}{F_{j} = {\frac{1}{10}{\sum\limits_{i = 1}^{10}\; m_{i,j}}}} & {{EQ}\mspace{14mu} 2}\end{matrix}$Here the subscript (i) represents rows 1-10 of the matrix (M) and thesubscript (j) represents the 1-280 columns of the matrix (X). Accordingto Equation 2, the common mode noise floor value (F) for each column (j)corresponds to the average or mean of the ten pixels values (m) in thecolumn.

Then through operation of the circuitry for each pixel (m) in thematrix, the value of the pixel minus the corresponding common mode noisefloor value (F) for the column (j) in which the pixel resides may becompared to a common mode noise hysteresis threshold value (T_(h)) asshown in Equation 3:

$\begin{matrix}{X_{i,j} = \begin{Bmatrix}{m_{i,j} \times G_{L}} & {if} & {{m_{i,j} - F_{j}} < T_{H}} \\{m_{i,j} \times G_{H}} & {if} & {{m_{i,j} - F_{j}} \geq T_{H}}\end{Bmatrix}} & {{EQ}\mspace{14mu} 3}\end{matrix}$

Here, if the difference between each pixel value and the correspondingcommon mode noise floor value (F) for the corresponding column is lessthen the hysteresis threshold value (T_(H)) then the pixel value in thematrix (X) is set through operation of the circuitry to a new valuecorresponding to the pixel multiplied by a low gain parameter (G_(L)).However, if the difference is equal to or greater than the hysteresisthreshold value (T_(H)) then the pixel value is set through operation ofthe circuitry to a new value corresponding to the pixel multiplied by ahigh gain parameter (G_(H)). In this described example embodiment thehysteresis threshold value, low gain parameter (G_(L)), and high gainparameter (G_(H)) are configurable parameters in the programinstructions associated with the circuitry of the IDM. Example valuesfor these parameters may include: T_(H)=4.0; G_(L),=0.0; and G_(H)=1.0.

In example embodiments after the magnetic image scan matrix has beenprocessed through operation of the circuitry to minimize the effects ofcommon mode noise, calculations involving passing the matrix through azero-phase low pass filter may be performed. In this described exampleembodiment, the filter may be applied through operation of the circuitryto each of the ten rows of the matrix (X) to produce another matrix (Y)according to Equations 4-6 as follows:

$\begin{matrix}{Y = \begin{bmatrix}Y_{1,1} & Y_{1,2} & \ldots & Y_{1,280} \\Y_{2,1} & Y_{2,2} & \ldots & Y_{2,280} \\\vdots & \vdots & \; & \vdots \\Y_{10,1} & Y_{10,2} & \ldots & Y_{10,280}\end{bmatrix}} & {{EQ}\mspace{14mu} 4}\end{matrix}$

$\begin{matrix}{Y_{i,j} = {{\frac{1}{{2w} + 1}{\sum\limits_{k = {j - w}}^{j + w}{X_{i,k}\mspace{14mu}{for}\mspace{14mu} j}}} > {{w\mspace{14mu}{or}\mspace{14mu} 280} - j} > w}} & {{EQ}\mspace{14mu} 5}\end{matrix}$

$\begin{matrix}{{Y_{i,j} = {{\frac{1}{{2\;\delta} + 1}{\sum\limits_{k = {j - \delta}}^{j + \delta}{X_{i,k}\mspace{14mu}{for}\mspace{14mu}\delta}}} = {w - j}}},{{j \leq {w\mspace{14mu}{or}\mspace{14mu}\delta}} = {280 - j}},{{280 - j} \leq w}} & {{EQ}\mspace{14mu} 6}\end{matrix}$

Here the subscript (i) represents rows 1-10 of the matrix (Y) and thesubscript (k) represents the 1-280 columns of the matrix (Y). Thesecalculations represent a moving average with a window of length 2*w+1,where w is the half window width. The average is calculated throughoperation of the circuitry by adding the current pixel (at X_(i,j)) anda predetermined number (w) of pixels before and a predetermined number(w) of pixels after the current pixel. This sum is then divided by thesum of: 2w+1. However as shown in Equation 6, when the current pixel isequal to or less than the predetermined number of pixels (w) from theedges of the matrix, the window width shrinks in size according to 2δ+1.In the described example embodiment the half window width number may bea configurable number of pixels in the IDM (e.g. w=3). Of course thisapproach is exemplary.

FIG. 20 shows a further example method wherein circuitry operates in amanner that conceptually divides a check 630 into six zones (zones 1-6)for purposes of determining whether a check is valid or potentiallyfraudulent. Here at least zone 2 and in some embodiments, thecombination of zones 1, 2 and 3 correspond to the MICR zone 632. In thisdescribed example embodiment the processor of the circuitry operates toidentify the area of the check which falls within 16 mm or some otherpredefined distance from the bottom edge 642 of the check as the MICRzone. The other zones 4, 5, and 6 form the check body or non-MICR zonewith its height being the check height less 16 mm or other predeterminedlength for the MICR section.

In addition in the exemplary approach the combination of zones 5 and 3may correspond to a leading blank zone (634) and the combination ofzones 4 and 1 correspond to a trailing blank zone (636). In thisdescribed example embodiment the processor of the circuitry identifiesthe area of the check which falls within 5 mm or some other predefineddistance from the leading edge 638 of the check as the leading blankzone. Likewise, the processor may identify the area of the check whichfalls within 5 mm or some other predefined distance from the trailingedge 640 of the check as the trailing blank zone.

In this described example embodiment, for a valid check the processor ofthe circuitry may be operative to determine that the MICR symbols have amagnetic presence as detected in the magnetic image scan matrix whichfalls in zone 2 (e.g within 16 mm of the bottom edge). Also, theprocessor may be operative to determine that a check is potentially afraudulent copy by determining that the magnetic image scan matrix showsthe presence of a magnetic signal in the leading and/or trailing blankzones (e.g. within 5 mm of the leading and/or trailing edges).

In addition an example embodiment may have circuitry that operates usingfuzzy logic rules for weighing the relevance of pixels in the leadingand trailing blank zones. For example, pixels in columns of the matrixcorresponding to portions of the check closest to the trailing andleading edges of the check may be assigned greater significance forpurposes of analysis than pixels in columns of the matrix correspondingto portions of the check adjacent the interfaces (645, 647) between theleading and trailing blank zones and zone 6.

For example, in one example embodiment, the columns of the magneticimage scan matrix which correspond to the leading and trailing blankzones may be identified by the processor of the circuitry and thecorresponding pixels in those columns may be multiplied by weighingfactors depending on their respective distance from the correspondingleading or trailing edges of the check. In an example embodiment of theIDM with a check transport speed of about 0.5 mm/ms, the leading andtrailing blank zones may include about five matrix columns each. In oneexample, the weighing factors may correspond to: 1, 1, 1, 0.5, 0.25.These five factors are multiplied by the pixels in the correspondingfive columns for each of the leading and trailing zones in the ordershown progressing from high to low values respectively for thecorresponding columns which progress inwardly from the edge of the checkto adjacent zone 6.

By having the processor of the circuitry associated with the IDM assign(through weighing factors) less significance to pixels in the leadingand trailing blank zones adjacent zone 6, the accuracy of the IDM may beincreased in cases where valid checks include stray magnetic ink nearthe leading and trailing blank zones. In alternative exampleembodiments, more than 5 pixels in each row adjacent the edges of thecheck may be used. For example in further example embodiments, eightpixels in from each edge of the check may be multiplied by weighingfactors such as (1, 1, 1, 1, 0.5, 0.25, 0.125, 0.0625). In the exampleembodiment, the processor of the circuitry is operative in accordancewith its programming to compare pixels in the matrix to a magneticpresence threshold (T_(P)). If the pixel value is at or above themagnetic presence threshold (T_(P)), the pixel may be regarded as being“dirty” or as having a magnetic presence. If the pixel value is belowthe magnetic presence threshold (T_(P)), the pixel may be regarded asbeing “clean” or as not having a magnetic presence. An example magneticpresence threshold (T_(P)) used to determine whether pixels are dirty orclean may correspond to a value of T_(P)=10. Thus pixels with values 10or greater may be considered dirty and pixels with values lower than 10may be considered clean. In the example embodiment, the magneticpresence threshold (T_(P)) may be configurable in the programming of thecircuitry associated with the IDM. When weighing factors are used, theweighing factors may be multiplied by the pixel values through operationof the circuitry before the pixel values are compared to the magneticpresence threshold (T_(P)).

In an example embodiment, when a zone has a total number of dirty pixelswhich is at or greater than a predetermined threshold for that zone,then the entire zone for that check may be considered as being dirty.For example with respect to the leading and trailing blank zones(referred together as the blank zone), if the blank zone has a totalnumber of dirty pixels at or above a blank zone threshold (T_(BD)), thenthe blank zone is considered to be dirty. If the total number of dirtypixels is below the blank zone threshold (T_(BD)), then the blank zoneis considered to be clean.

In an example embodiment, the blank zone threshold (T_(BD)) may be aconfigurable parameter in connection with the associated analysiscircuitry. In addition, the blank zone threshold (T_(BD)) may also varydepending on the size of the check detected by the IDM. For example, fora relatively larger business check (vertical height greater than 68 mmfor example) the processor may operate in accordance with itsprogramming to use a blank zone threshold such as T_(BD)=18. However fora relatively smaller personal check (vertical height less than or equalto 68 mm for example) the processor may use a relatively smaller blankzone threshold such as T_(BD)=15.

In addition to the leading and trailing blank zones, the processor mayalso operate to classify the pixels in zone 6 or the background zone ashaving either dirty or clean pixels by comparing the pixels to themagnetic presence threshold (T_(P)) value. Here if zone 6 has a totalnumber of dirty pixels at or above a zone 6 threshold (T_(Z6D)), thenzone 6 is considered to be dirty. If the total number of dirty pixels isbelow the zone 6 threshold (T_(Z6D)), then zone 6 is considered to beclean. In an example embodiment, zone 6 threshold (T_(Z6D)) may be aconfigurable parameter with a default value such as T_(Z6D)=25.

In example embodiments, the classification by the circuitry of zone 6 asdirty does not necessarily indicate that the check is a copy. Asdiscussed previously, a valid check may also include magnetic ink inzone 6 of a check. However, the presence of magnetic material in zone 6may indicate that the current check has a relatively higher probabilityof being a copy, which among other factors evaluated by the processormay cumulatively result in the check being classified as a potentiallyfraudulent copy.

As discussed previously, the circuitry associated with the exemplary IDMis operative to determine if the MICR zone includes a magnetic presence.This determination may also be made by determining the number of pixelsin the MICR zone which are at or above the magnetic presence threshold(T_(P)). FIG. 21 shows an example of scanning paths for the tendifferent magnetic sensing elements or channels 670 of the examplemagnetic sensor superimposed on a check 672. Each of the ten sensorchannels correspond to the 10 rows of the above described magnetic imagescan matrix. The pixels in the first two channels 674, 676 (or rows)adjacent the bottom edge of the check is used to determine if the MICRline includes a magnetic presence and whether or not the MICR zone isdirty or clean.

As shown in Equation 7, the processor is operative to calculate for eachcolumn of the matrix, the mean square sum (S) of pairs of pixels (P1)and (P2) in the column which are from the first and second channels(674, 676) respectively of the sensor (or rows of the matrix).S=√{square root over (P ₁ ² +P ₂ ²)}  EQ 7Here S corresponds to the combined MICR pixel for the two rows orchannels adjacent the MICR zone. If the pixel for a given row from thesecond channel (P₂) has a value of zero and the combined MICR pixel (S)for the row is greater than the presence threshold (T_(P)), then thecombined pixel is considered to be a dirty pixel and is not used todetermine if the MICR line is present. However, if the value of thepixel in the second channel (P₂) is not equal to zero and the combinedMICR pixel (S) for the given row is greater than the presence thresholdT_(P), then the combined pixel (S) is considered to indicate that MICRis present.

If the total number of combined pixels in the MICR zone which indicatethat MICR is present is equal to or greater than a MICR presencethreshold (T_(MP)) than the MICR line may be classified as beingpresent. Whereas if the total of the MICR present pixels is less thanthe MICR presence threshold (T_(MP)) than the MICR line may beconsidered absent. In an example embodiment, the MICR presence threshold(T_(MP)) may be a configurable parameter of the IDM with a default valuesuch as T_(MP)=40.

A determination that the MICR line is present may weigh in favor of thecheck being valid. However, in addition to determining whether the MICRline is present, the processor may also determine whether the MICR zoneis dirty. For example if the total number of dirty pixels in the MICRzone is greater than or equal to a MICR dirty threshold (T_(MD)), thenthe MICR zone is considered to be dirty. A dirty MICR zone is a strongindicator that the check is a copy. In an example embodiment, MICR dirtythreshold (T_(MD)) may be a configurable parameter through theprogramming executed through operation of the circuitry with a defaultvalue such as T_(TD)=5. In this described example embodiment, adetermination as to whether MICR pixels are clean or dirty may only beperformed on MICR right checks. All MICR pixels may be considered to beclean for MICR left checks.

In an example embodiment, the processor of the circuitry may classify acheck as good or a potential fraudulent copy responsive to a table orset of rules which define whether the MICR line is present or absent anddefines for each zone whether the zone includes dirty or clean pixels.

FIG. 22 shows an example of such a table 650.

In further example embodiments, the processor may be operative toevaluate the presence or absence of dirty or clean pixels in each of thezones, and other characteristics of the pixels in the magnetic imagescan matrix to derive a confidence level for the check ranging from highto low. Here a high confidence level indicates a high probability thatthe check is valid and a low confidence level indicates a lowprobability that the check is valid. For example as shown in the tablein FIG. 22 (at the row referenced with reference numeral 651), a checkwith: a MICR line present in the MICR zone, clean pixels in the leadingand trailing zones, and a clean zone 6 may correspond to a highconfidence level that the check is good. As a result the processor mayclassify the check as being good. However, if such a check has even onedirty pixel in the zone 6, the confidence level may drop to a mediumlevel (as shown in the row referenced with reference numeral 652).Depending on the sensitivity setting of the IDM, such characteristicsmay still result in the check being classified as being good as long asthe blank zone and MICR zone have clean pixels and the MICR line ispresent in the MICR zone.

However, in further example embodiments, the processor may be operativein accordance with its programming to evaluate other characteristics ofthe pixels in zone 6 or elsewhere to determine a confidence level for acheck. For example, if the majority of zone 6 includes dirty pixels,whereas valid checks statistically have a reactively lower number ofdirty pixels or lower intensity dirty pixels, then the processor may beoperative to assign a low confidence level to the check even thoughmagnetic ink is allowed in zone 6 of valid checks.

In example embodiments, the rules represented in the table 650 shown inFIG. 22 or alternative sets of rules for different and/or additionaltypes of zones of the check may be implemented in the programmingassociated with the processor to calculate the confidence level. Theprogramming associated with the IDM circuitry may then include aconfigurable sensitivity setting which is compared to a determinedconfidence level for a check to determine whether the check is good oris a copy. Also, in further example embodiments, information obtainedregarding the magnetic image map may be combined with magnetic symbolrecognition of the MICR line symbols, optical features of the check,optical symbol recognition (OCR) information obtained from the check,and/or other information obtained from the check for use withdetermining a confidence level for the check.

In example embodiments, responsive to operation of at least oneprocessor, the ATM may transfer image data corresponding to opticalscans (front and back) of the check to a server remote from the ATM. TheIDM initially may generate grayscale images of the front and back of thecheck. However, the server which receives electronic images of thecheck, may prefer the images to be saved in a black and white formatwhich may have a smaller file size. As a result, the processor of thecircuitry in the ATM may be operative to convert each grayscale image toa black and white equivalent based on a threshold that is set as thedividing line for assessing grayscale values as either black or white.In an example embodiment, the above described tests of the optical imagescan may be performed on the grayscale image, the black and white image,or both types of images.

In one example embodiment, the processor may be operative to generate aplurality of different black and white images from each scannedgrayscale image. Each black and white image may be generated responsiveto a different threshold value for determining whether to convert agrayscale pixel to either a white or black pixel. Each of thesedifferent black and white images may be evaluated based on one or moreof the previously described tests. The thresholds may be determinedbased on stored values in a database, or based on a range of sensedgrayscale values in the image, for example. The processor may thenoperate to select the black and white image which has the highestrelative confidence level to transfer to a server associated with theATM. A high confidence level in exemplary embodiments may be based on,for example, a percentage range of pixels that is generally light ordark within a usable check image. The image that best falls within thecenter of this range may be one selected through operation of theprocessor. Of course this approach is exemplary.

In example embodiments, the at least one processor may be operative inaccordance with its programming to detect the location of the magneticMICR line (whether on top or bottom of a document and/or whetheradjacent an upper or lower edge of the document). Responsive to thisdetermined location of the MICR line, the at least one processor isoperative to set the likely orientation of the check for purposes ofevaluating optical characteristics of the check as described previously.However, in documents without known or consistent magnetic features suchas a MICR line, the orientation of the document may not be determinablebased on a magnetic scan. In such cases, the at least one processor mayoperate to determine the orientation of the document by evaluating aspecific field (e.g. account number field) on the document with symbolsof a particular font type. The processor may use data corresponding to atemplate stored in a data store to define a window on the image wherethe given field is expected to be located. The template may also haveassociated data that defines the font type expected to be present in thewindow, a minimum/maximum number and/or specific types of symbols (e.g.“:” or “<” symbols) expected to be present in the window, and/or othercharacteristics or tests used to determine a confidence level for thefield being evaluated.

In this described embodiment, both a top and a bottom faces of thedocument are optically scanned. The pixels in a field of one of thescanned images (e.g. the top scan) of the document may be evaluatedthrough operation of a processor which operates in accordance with itsprogramming to first assume the document was in a first orientation whenscanned. Such an assumed orientation may correspond to the side of thedocument containing the field to be evaluated being orientated face upand rotated in a particular manner. In some example embodiments thefirst orientation corresponds with the face up, right side uporientation shown in FIG. 40. Of course this approach is exemplary. Forthe assumed first orientation, if the confidence level resolved by theprocessor for the field is above a predetermined threshold and/or one ormore other tests are consistent with the field having symbols specifiedby the template, then the processor is operative to process the imagesas described previously based on the document being determined to be inthe first orientation.

However, if a determined confidence level for the field is below apredetermined threshold and/or one or more other tests are inconsistentwith the field having symbols specified by the template, the processormay be operative to reevaluate the data corresponding to the field inthe previously evaluated scanned image (e.g. top scan) and process theimage data based on the document being in a second orientation, such asbeing rotated 180° with respect to the first orientation. In someexample embodiments the processor may operate in accordance with itsprogramming such that the second orientation may correspond to the faceup, upside down orientation shown in FIG. 41. For the second orientationif a determined confidence level as determined responsive to operationof the process or for the field is still below a predetermined thresholdand/or one or more other tests are inconsistent with the field havingsymbols specified by the data included with the template, the processormay be operative to reevaluate the data comprising the image to evaluatedata corresponding to the field in the other one of the scanned images(e.g. bottom scan). The processor may evaluate the image data based onthe document being in a third orientation such as when the document isoriented face down. In some example embodiments the third orientationmay correspond to the face upside down, right side up orientation shownin FIG. 42. For the third orientation if a determined confidence levelas determined by the processor for the field is still below apredetermined threshold and/or one or more other tests are inconsistentwith the data that corresponds to the field in the current assumedorientation having symbols specified by the template, the processor maybe operative to reevaluate the data based on the field in the previouslyevaluated scan image (e.g. bottom scan) assuming the document was in afourth orientation such as when the document is face down and rotated180° with respect to the third orientation. In some example embodimentsthe fourth orientation may correspond to the face upside down, rightside up orientation of FIG. 43. For each of these differentorientations, if the confidence level as determined by the processorthat the desired field has been located is below a predeterminedthreshold and/or one or more other tests are inconsistent with the fieldhaving symbols specified by the template, the document may be rejectedand returned to the customer. Of course this approach is exemplary andin other embodiments other approaches may be used, such as to repeat thesteps or to attempt to apply data corresponding to another template tothe image data.

In this described embodiment, the processor is operative in accordancewith its programming to store data of received documents in anassociated data store and determine through analysis of stored data whena predetermined number of documents being scanned are consistentlyor/are predominantly in the second or other orientation rather then thefirst orientation. In response to this detection, the processor may beoperative in accordance with its programming to begin testing datacorresponding to subsequent images assuming the documents being scannedare initially in the second or other orientation rather than the firstorientation. Of course this approach is merely exemplary.

In example embodiments, the magnetic sensor may be used to read thenumeric symbols and/or other symbols which are present in the MICR line.In some embodiments the magnetic sensor may correspond to a magneticread head which generates electrical signals responsive to magneticindica moving adjacent thereto. Of course this type of sensor isexemplary of sensors that may be used. In one example embodiment, aseach symbol of the check moves horizontally across the sensor, anelectric signals are output which comprise a waveform. The waveformvaries responsive to the variation in density of magnetic ink from theleading edge of each MICR symbol to the trailing edge of the symbol.FIG. 23 shows an example of different numeric symbols (0 through 9) andnon-numeric symbols (Transit, Amount, On-Us, and Dash) for the U.S.standard MICR E-13B font and visual representations of theircorresponding magnetic waveforms as generated by an example embodimentof the magnetic sensor.

In some example embodiments a document with MICR symbols may havemagnetic sensor outputs digitally sampled through operation of amagnetic reading device and appropriate circuitry. The circuitry mayinclude for example, analog to digital converters and one or moreprocessors with associated stored program instructions. In an exemplaryembodiment the document may be within an ATM and be moved in a transportof a check acceptor with a transport speed of about 500 mm/sec. At thattransport speed the sampling interval of an exemplary embodiment may beabout 63.5 μs. In some example embodiments with about a 0.125 (±0.01)inch symbol spacing (as defined in ANSI X9.27), there may be about 100(±8) samples taken for each E13B symbol. Sampled signal data taken fromthe original magnetic waveform may be defined as the raw signal {u}={u₀,u₁, u₂, . . . u_(n-1)}. In some example embodiments each sample may bean eight bit unsigned integer. In some example embodiments the data byteseries with a fixed sampling interval of 63.5 μs may have a data seriesthat may have a length denoted as N. In some example embodiments the rawsignals may be the magnetic sensor outputs may be pre-amplified orfiltered before being converted through an analog to digital converterand other appropriate circuitry to into the raw digital samples {u}.

In some example embodiments the average μ of the raw signal may becalculated through operation of at least one processor as in Equation 8.

$\begin{matrix}{\mu = {\frac{1}{N}{\sum\limits_{i = 0}^{N - 1}u_{i}}}} & {{EQ}\mspace{14mu} 8}\end{matrix}$In some example embodiments the standard deviation σ may be calculatedthrough operation of at least one processor as in Equation 9.

$\begin{matrix}{\sigma = {\sqrt{\frac{\sum\limits_{i = 0}^{N - 1}( {u_{i} - u} )^{2}}{N - 1}} = \sqrt{\frac{{\sum\limits_{i = 0}^{N - 1}u_{i}^{2}} - {N \cdot \mu^{2}}}{N - 1}}}} & {{EQ}\mspace{14mu} 9}\end{matrix}$

In some example embodiments the processor may operate in accordance withits programming such that the raw data series {u} is baseline corrected.In a graphical representation of the steps executed by the processor thebaseline correction may be considered as helping to more accuratelycenter possible positive and negative peaks of the sampled magneticwaveform on the Y axis. In some example embodiments the baselinecorrection may be based on the average μ as calculated in Equation 8. Insome embodiments the average may be recalculated for each sample beingbaseline corrected. In yet other example embodiments a fixed number ofraw samples may be baseline corrected and then a new average μ may becalculated to be used for subsequent base line corrections. Of coursethese approaches are exemplary.

After baseline correction, the at least one processor operates to usethe raw data series {u} to resolve the corrected data series {x}={x₀,x₁, x₂, . . . , x_(N-1)} where x_(i)=u_(i)−μ. High frequency noise maybe present and impact the sample magnetic waveform due to motor noisefrom motors operating within the ATM. In some example embodiments thecorrected data series {x} may be input to circuitry which includes afilter function operative to boost the signal to noise ratio byattenuating high frequency noise. The output of the circuitry includingthe filter function may be defined as the series {y}. In some exampleembodiments circuitry including a low pass filter may be used to filterout only the high frequencies. In some example embodiments an infiniteimpulse response (IIF) filter may be used to filter the series {x}. Insome example embodiments circuitry including a Bessel filter may beused. In yet other example embodiments a 10^(th) order Bessel filter maybe used to filter the corrected data series {x}. In some exampleembodiments the filter corner frequency may be set to about 8% of thesampling frequency. In some example embodiments the Bessel filter mayhave a corner (cutoff) frequency set at 1259.84 Hz, which is 8% of thesample frequency of 63.5 μs. FIG. 37 shows a graph representing thefilter output {y} for an example magnetic waveform. Some of the highfrequency peaks may have some attenuation do to the filtering throughthe circuitry in operative connection with the magnetic read head.

In some example embodiments the Bessel filter may comprise a recursivefilter. A Bessel filter with a gain G=17114.10772 may be expressed as inEquation 10.

$\begin{matrix}\begin{matrix}{{y\lbrack n\rbrack} = ( {( {1*{x\lbrack {n - 10} \rbrack}} ) +} } \\{( {10*{x\lbrack {n - 9} \rbrack}} ) +} \\{( {45*{x\lbrack {n - 8} \rbrack}} ) +} \\{( {120*{x\lbrack {n - 7} \rbrack}} ) +} \\{( {210*{x\lbrack {n - 6} \rbrack}} ) +} \\{( {252*{x\lbrack {n - 5} \rbrack}} ) +} \\{( {210*{x\lbrack {n - 4} \rbrack}} ) +} \\{( {120*{x\lbrack {n - 3} \rbrack}} ) +} \\{( {45*{x\lbrack {n - 2} \rbrack}} ) +} \\{( {10*{x\lbrack {n - 1} \rbrack}} ) +} \\{( {1*{x\lbrack {n - 0} \rbrack}} ) +} \\{( {{- 0.0005222117}\mspace{11mu}*{y\lbrack {n - 10} \rbrack}} ) +} \\{( {0.0090729955\mspace{11mu}*{y\lbrack {n - 9} \rbrack}} ) +} \\{( {{- 0.0730293973}\mspace{11mu}*{y\lbrack {n - 8} \rbrack}} ) +} \\{( {0.3598632735\mspace{11mu}*{y\lbrack {n - 7} \rbrack}} ) +} \\{( {{- 1.2073089239}\mspace{11mu}*{y\lbrack {n - 6} \rbrack}} ) +} \\{( {2.8966760837\mspace{11mu}*{y\lbrack {n - 5} \rbrack}} ) +} \\{( {{- 5.0677050314}\mspace{11mu}*{y\lbrack {n - 4} \rbrack}} ) +} \\{( \;{6.4412468866\;*{y\lbrack {n - 3} \rbrack}} ) +} \\{( {{- 5.7663157314}\mspace{11mu}*{y\lbrack {n - 2} \rbrack}} ) +} \\{{ ( {3.3481883790\mspace{11mu}*{y\lbrack {n - 1} \rbrack}} ) )/G};}\end{matrix} & {{EQ}\mspace{14mu} 10}\end{matrix}$

In some example embodiments the circuitry may operate such that afterthe raw magnetic waveform has been baseline corrected and filtered, azone of the filtered data stream {y} may be selected through operationof at least one processor. The zone may be defined as a sub series ofconsecutive data samples from the data stream {y}. In some exampleembodiments the zone may be a special zone centered around a determinedpeak value of the sampled signal data. In some examples the zone maycontain five sample values on each side of the peak. In some exampleembodiments a zone centered at a peak may be represented by the value hwwhere hw is the zone half width value. In some embodiments hw=5 so thatthe zone length is 2*hw+1=11. In some example embodiments a zone with hwhas 11 data stream {y} values. The weight, cut, cut series and anchordepth for a zone may be calculated through operation of at least oneprocessor using the data stream {y} values as will be discussed later.The at least one processor operates in an exemplary embodiment togenerate a data value corresponding to the zone associated with eachpeak.

The exemplary filters that have been discussed comprise digital filters,but in other embodiments other types of filtering may be used. Thefilter or filters may be analog filters, passive or active, pipelined,switch capacitor, or any other suitable filter. In some embodiments thefiltering of the raw magnetic signal may use a combination of differentfilters.

In one example embodiment, to magnetically recognize the particular MICRsymbols, each detected magnetic waveform for each of the MICR symbolsmay be evaluated through operation of at least one processor to identifypeaks in the corresponding sensed magnetic waveform generated responsiveto signals from one or more magnetic sensors. Characteristics of each ofthe peak positions (as described below) may be determined to cause atleast one processor to resolve a feature vector ({right arrow over(v)}). Different peak values of the same magnetic waveform maycorrespond to different elements of a feature vector. Each detected MICRsymbol may in a graphic representation have a set of peaks whichdetermine the feature vector elements, and therefore a different featurevector. The peaks may be positive peaks or negative peaks, therefore thefeature vector element values may be positive or negative. In anexemplary embodiment each of the feature vectors may be comprised ofeight peak values. Each peak value may correspond to a peak amplitudevalue which corresponds a feature vector element value. The processormay operate to compare each sampled feature vector for the sensed MICRsymbol to a standard feature vector for each of the fourteen standardMICR E-13b symbols. The processor operates such that the standard E-13Bsymbol which has the highest correlation to the detected waveform may beidentified as the recognized symbol for the detected magnetic waveform.FIG. 24 shows an example of a table of the MICR E-13b symbols (columnlabels) and their corresponding determined peak features which comprisetheir respective feature vector. Here the values associated with thepeaks for each symbol are shown in order of their detection in time fromthe top of the table to the bottom in each column of the table. In someexample embodiments the peaks may correspond to eight fixed locationsequally spaced transversely across each symbol. In some exampleembodiments the locations may be equally spaced apart in a time domainbased on a constant speed of the document including the symbol movingpast the magnetic sensor. Of course this approach is exemplary and inother embodiments other approaches may be used. This includescorrelation with document position through sensing position of thedocument relative to the magnetic sensor, for example.

FIG. 30 shows an example graphic representation magnetic waveform thatmay correspond to the MICR E-13B symbol representing the number “8.” Theexample waveform shown in FIG. 30 graphically shows the eight featurevector locations equally spaced apart. FIG. 30 graphically showspositive peaks at feature position 2, 3, and 6 and negative peaks areshown at vector feature positions 3, 7 and 8. There are no peaks atvector positions 4 and 5 where the magnetic waveform is essentiallyzero. The feature vector element values in FIG. 30 as represented inthis graphic coordinate system correspond to the value {right arrow over(v)}₈=(79, 86, −99, 0, 0, 105, −73, −86)^(T) which corresponds to thesymbol for the number “8.” It should be understood that these values areexemplary based on the coordinate system applied, and in otherembodiments other values may be used to represent the relativemagnitudes of the resolved data values.

In another example embodiment, to magnetically recognize the MICRsymbols, each detected magnetic waveform for the MICR symbols areevaluated through operation of at least one processor of the circuitryassociated with the IDM to conduct calculations that essentiallyidentify peaks in the magnetic waveform. The relative distances betweenthe immediately adjacent peaks is then determined. Characteristics ofthe distances between each adjacent peak (as described below) may bedetermined to form a feature vector ({right arrow over (v)}) where eachfeature vector element corresponds to a physical distance on the checkbetween adjacent signal peaks. Each detected symbol will have adifferent set of distances between adjacent peaks and thus a differentfeature vector. The distances between each peak may be characterized aseither a long “L” or a short “S” distance. The relative distancesbetween adjacent positive peaks may correlate to the distance betweennegative peaks. Each of the feature vectors in an exemplary embodimentmay be comprised of six elements and thus six peak distance values. Whenthe feature vector is comprised of six peak distance values, the MICRwaveform may be comprised of seven peaks.

In some embodiments the predetermined feature vector for the detectedsymbol may be analyzed through operation of the circuitry forcorrelation with the feature vector for each of the fifteen standardMICR CMC-7 symbols. The standard MICR CMC-7 symbol which has the highestcorrelation and generally corresponds to the sample feature vector forthe detected waveform as determined through operation of a processor inthe circuitry is identified as the recognized symbol for the detectedmagnetic waveform. FIG. 31 shows an example of a table of the MICR CMC-7symbols (column labels) and their corresponding distances between peaks(element values) which comprise their respective predetermined symbolfeature vector. In this example table the short distance between peaksis represented by the distance value of “10” and the long distancebetween peaks is represented by the distance value “15.” Of course thesevalues are exemplary and in other embodiments other values thatcorrespond to distances may be used. Here the values associated with thepeak distances for each symbol are shown in order of their position withrespective adjacent peak distance values from the top of the table tothe bottom in each column of the table. In some example embodimentsseven valid peaks may be detected for a valid CMC-7 MICR symbol. In someexample embodiments the seven valid peaks would correspond with sixvalid distances between peaks corresponding to feature vector elements.Notice that each feature vector for a MICR CMC-7 symbol in FIG. 31always has exactly four short distances and two long distances betweenadjacent peaks and this may be useful when detecting missing or extrapeaks which is discussed later.

Notice in the example vector table of FIG. 31 for the CMC-7 magneticsymbols, that if any of the feature vectors element values are reversed(the vector element values flipped) that the reversed feature vectorwill correspond to another different valid feature vector. For example,take the vector for “1” which is {right arrow over (v)}=(15 10 10 10 1510). If this vector is reversed the vector becomes {right arrow over(v)}=(10 15 10 10 10 15) which corresponds to “T.” Because every vectorhas a corresponding vector when read in reverse, the detection ofwhether a check is upside down and being read in reverse requiresadditional analysis, as will be discussed later.

FIG. 33 shows an example of a graphical representation of a magneticwaveform that corresponds to output signals of an exemplary sensorsensing the MICR CMC-7 symbol representing the number “8.” Example FIG.33 graphically shows the seven detected positive peak values. Theexample magnetic waveform in FIG. 33 shows a long distance betweenpositive peaks 2-3 and 5-6. The example in FIG. 33 shows short distancesbetween positive peaks 1-2, 3-4, 4-5 and 6-7. Notice that in thisembodiment the negative peaks are not needed and only the positive peaksare used to measure distances between peaks to derive the featurevector. Additionally the negative peaks may correspond to the samefeature vector. Alternatively, in some embodiments only the negativepeaks may be used to determine the distance between peaks to determinethe elements of a corresponding feature vector. Because in thisembodiment the negative and positive peaks may each be used to determinethe same feature vector, this redundancy may be used by the circuitry todetermine which peak is invalid when more than 7 peaks are detected ormay be used by the circuitry to find missing peaks as discussed later.The feature vector element values in FIG. 33 correspond to the value forthe number “8” {right arrow over (v)}₈=(S=10 L=15 S=10 S=10 L=15S=10)^(T).

FIG. 34 shows schematically steps in an example method 1000 fordetecting MICR symbols using feature vectors. The method is carried outby an ATM responsive to computer executable instructions carried out byat least one processor in the ATM. The computer executable instructionsmay reside on suitable media such as a hard drive, flash memory, CD,DVD, or other form of volatile or non-volatile computer memory. Themethod begins at step 1002 where a check may be received at an ATM. Insome example embodiments, documents that contain MICR symbols other thana check may be received. The check may be received by a suitable checkacceptor. Next at step 1004 the check is transported into the ATM by oneor more suitable transport devices. While the check is being transportedit may be moved adjacent to one or more sensors capable of detectingmagnetic ink. At step 1006 magnetic signals are acquired from the check.In some embodiments the signals generated by the magnetic sensor maycomprise an analog electrical signal. This signal may be convertedthrough appropriate circuitry such as circuitry including an analog todigital converter through a corresponding digital signal. The magneticsignals may be digital signals derived from samples of the signalsdetected with the one or more magnetic sensors in step 1006 or may bedigital signals directly sampled by the sensors in step 1006. In someexample embodiments the sampled digital signal data may correspond tovalues for a MICR symbol that has been sampled about 100 times while thesensor is sensing the particular symbol. Of course this approach isexemplary.

In some example embodiments the sampled digital signal data may beanalyzed and used to generate a plurality of data values, each of whichcorrespond to a magnetic peak waveform peak. The peak value maycorrespond to an area of the waveform that has been sampled throughoperation of the circuitry of the machine about 11 times. Such sampledsignal data values are stored in memory as appropriate for analysis.Next, at step 1008 data values are generated from the signals acquiredin step 1006. The generated data values correspond to the sensed MICRsymbol. The data values may be a subset of the digital signals. In someexample embodiments the data values may correspond to magnetic signalwaveform peaks and may correspond to the amplitude of those peaks. Thegenerated data values may alternatively be based on a function of thesampled signal data in the area of each peak. The generated peak datavalues are also stored as appropriate. At step 1010 the at least oneprocessor operates to calculate a sample magnetic data feature vector isproduced corresponding to the data values generated in step 1008.

In some embodiments the elements of the sample magnetic data featurevector may correspond to magnetic waveform peak values which correspondto the maximum amplitudes. In some example embodiments the samplemagnetic feature vector element values may be positive or negativecorresponding to positive or negative peaks. In other exampleembodiments the magnetic data feature vector elements and thepredetermined feature vector elements may correspond to fixed locationswhere a MICR symbol may have been sampled. These may or may notnecessarily correspond to peaks. In some example embodiments the fixedlocations across each MICR symbol may be eight fixed locations as shownin FIG. 30. In some example embodiments the eight locations are aboutequally spaced apart in the time domain based on constant relative speedbetween the document bearing the MICR symbol and the magnetic sensor. Insome example embodiments the feature vector may correspond to a symbolin the E-13B font.

In some example embodiments the magnetic data feature vector elementvalues may correspond to the distance between adjacent peaks. In yetother example embodiments the magnetic data feature vector elementvalues may correspond to the distance between adjacent peak for fixednumber of peaks. In some example embodiments the fixed number of peaksmay be seven with six distance elements in the magnetic data featurevector. In some example embodiments the feature vector may correspond toa symbol in the CMC-7 font. In some example embodiments the distancebetween peaks may correspond to the distance between positive peaks orthe distances between the negative peaks.

The exemplary method continues in step 1012 where the resolved magneticsample data feature vector is compared through operation of at least oneprocessor to data in at least one data store corresponding to each of aplurality of predetermined symbol feature vectors each one of whichcorresponds to one MICR symbol. A suitable comparison may be performedresponsive to operation of a processor that may be operative todetermine which one of the predetermined sample feature vectors, thesensed magnetic data sample feature vector corresponds. In some exampleembodiments the vectors may be compared using a Pearson correlation. Atstep 1014 a determination is made as to which predetermined symbolfeature vector the magnetic data sample feature vector generallycorresponds. In some embodiments in step 1012 the magnetic data featurevector may be compared to each of a predetermined set of standard MICRsymbol feature vectors using a Pearson correlation. In some embodimentsthe determination in step 1014 may be responsive to a Pearsondetermination. The at least one processor may also operate in accordancewith its programming to identify the beginning and end of each of theMICR symbols based on resolved signal gaps and/or signal fluctuationsthat are identifiable as associated with areas between the symbols. Theprocessor operates responsive to determining that the sample featurevector generally corresponds to one predetermined symbol feature vector,to generate symbol data. The symbol data corresponds to the symbolrepresented by the predetermined symbol feature vector and the symbolsensed on the check. The processor then operates to store the symboldata for use on the check. The processor then operates to store thesymbol data for use in accordance with the programming associated withat least one processor of the machine. For example, such use may includeincorporating signal data in a message sent by the machine to a remotecomputer for purposes of carrying out a transaction.

In response to determining one MICR symbol with a sufficient degree ofcorrelation, the exemplary at least one processor then operates inaccordance with its programming to analyze additional sensed datacorresponding to other symbols. The process steps 1008, 1010, 1012 and1014 are repeated to determine all of the MICR symbols read from thecheck.

The at least one processor then operates in accordance with itsprogramming to determine if all of the MICR symbols on the check couldbe successfully determined. If all of the MICR data could be read andresolved, the at least one processor may operate in accordance with itsprogramming to determine if the check can be accepted by the machine.This may include for example, the machine operating to forward at leasta portion of the MICR data with other data such as the check amount,card data and PIN data input by the ATM user, and other data from theATM to a remote computer. The remote computer may operate in accordancewith its programming and data stored in connection with the remotecomputer to determine if the check should be accepted. The remotecomputer may then operate to send at least one message to the ATM. Themessage includes data corresponding to whether or not the check shouldbe accepted. If so, the at least one processor in the ATM may operate tocause the check to be stored in the ATM. This is indicated by a step1016. If all the MICR symbols on the check could not be resolved, or theremote computer determines that the ATM should not accept the check, theat least one processor may operate to cause the ATM to return the checkto the customer. Of course these approaches are exemplary and in otherembodiments other approaches may be used.

In an example embodiment, the peak detection may begin through operationof the at least one processor identifying data corresponding to theamplitude and its associated time along the magnetic waveform for allpotential minimum and maximum peaks in the waveform for a detectedsymbol. FIG. 25 shows a portion of an example detected magnetic waveformfor a detected MICR symbol including two peaks (722, 724).

Detected magnetic waveforms may include thin spikes which may berecognized as peaks but are actually the result of transient noise(spikes). The presence of such peaks may distort the feature vector fora given symbol making it difficult to accurately recognize the symbol.However, true peaks may have a substantial area under the curve of thepeak compared to transient noise spikes. Thus the example embodimentincludes at least one processor that is operative to determine a weightvalue for each peak which corresponds to an area under the curve of thepeak. Only peaks which have a weight above a predetermined threshold maybe classified as true peaks for purposes of determining the values ofthe feature vector for a detected MICR symbol.

In an example embodiment, the weight for each peak may be calculated bya processor integrating the portion of the magnetic waveform whichcorresponds to the peak. Equation 11 shows an example of the calculationused to determine the weight (w) of a peak found at time (x) for thedetected magnetic waveform of a MICR symbol on a check.

$\begin{matrix}{{w(x)} = {\int_{x - {hw}}^{x + {hw}}{{f(t)}{\mathbb{d}t}}}} & {{EQ}\mspace{14mu} 11}\end{matrix}$Here f(t) is the detected magnetic waveform and hw is the same halfwindow width discussed earlier. The shaded areas shown in FIG. 26 showan example of the calculated areas or weights determined for the peaks722, 724

Although the weight of a peak may be used to distinguish true peaks fromtransient noise spikes, the weight of a peak may be very sensitive tobaseline (offset) drift by the magnetic sensor. As a result a relatively“flat” peak due to baseline drift could have a significant weight value,while a true peak with low amplitude (like the fifth peak in E13B symbol“7” (generally shown with reference numeral 700 in FIG. 23) may have avery small weight.

In other sample embodiments the weight of a peak may be calculated fromdigital samples taken from a magnetic waveform. In some exampleembodiments the digitally sampled signals may be represented by y_(i).In some example embodiments the weight of a peak centered at y_(i) isrepresented by w_(i) where the weight of the left side of a peak isrepresented by w_(i, left) and is calculated according to Equation 12.

$\begin{matrix}{w_{i,{left}} =  {\sum\limits_{j = {i - {hw} - 1}}^{i - 1}y_{j}} \middle| \mspace{14mu}{{if}\mspace{14mu} y_{i}\mspace{14mu}{has}\mspace{14mu}{same}\mspace{14mu}{polarity}\mspace{14mu}{as}\mspace{14mu}{yi}} } & {{EQ}\mspace{14mu} 12}\end{matrix}$where hw represents the peak half width discussed earlier such thatthere are exactly hw magnetic samples before and after the identifiedpeak. The weight of the right side may be represented by w_(i, right)and is calculated according to Equation 13.

$\begin{matrix}{w_{i,{right}} =  {\sum\limits_{j = {i + 1}}^{i + {hw}}y_{j}} \middle| \mspace{14mu}{{if}\mspace{14mu} y_{i}\mspace{14mu}{has}\mspace{14mu}{same}\mspace{14mu}{polarity}\mspace{14mu}{as}\mspace{14mu}{yi}} } & {{EQ}\mspace{14mu} 13}\end{matrix}$If the peak is a positive peak the peak weight may be calculatedaccording to Equation 14.w _(i)=2·min(w _(i,left) ,w _(i,right))+y _(i)  EQ 14If the peak is a negative peak, the peak weight may be calculatedaccording to Equation 15.w _(i)=2·max(w_(i,left) ,w _(i,right))+y _(i)  EQ 15

In some example embodiments the weights of a peak may be calculatedthrough operation of at least one processor and used as element valuesin a feature vector corresponding to a magnetic waveform representing aMICR symbol. The feature vector with elements corresponding to peakweight values may then be compared for correlation through operation ofa processor with data corresponding to a predetermined set ofcorrelation vectors of a MICR font. Based on the results of thecorrelation a particular MICR symbol is identified as corresponding tothe magnetic waveform. The processor then provides at least one outputcorresponding to the determined symbol.

In some example embodiments the feature vector may have eight elementswhere the elements correspond to a possible magnetic waveform peaklocation. The feature vector {right arrow over (v)}_(x) with eightelements representing eight possible peak locations may be representedby Equation 16.{right arrow over (v)} _(x)=(p ₁ p ₂ p ₃ p ₄ p ₅ p ₆ p ₇ p ₈)^(T)  EQ 16The possible peaks may be represented as p_(k) and the weight of apossible peak may be represented in Equation 17 as w_(i).p _(k)=0 if no peak found at position kp _(k)=w_(i) if a peak found at position k  EQ 17The weight values of possible peaks in {right arrow over (v)}_(x) in maybe calculated as in any of the methods described earlier.

An example embodiment of a method 1100 of using peak weights to detectMICR symbols from a magnetic waveform is shown in FIG. 35. The method iscarried out by an ATM responsive to at least one processor executingcomputer executable instructions. The method begins at step 1102 where acheck is received in an ATM. In some example embodiments documents thatcontain MICR symbols other than a check may be received. Next at step1104 the check is transported into the ATM by one or more suitabletransport devices. While the check is transported it may be movedadjacent to one or more sensors such as read heads capable of detectingmagnetic ink. At step 1106 magnetic signals are acquired from the check.The magnetic signals may be digital signals derived through operation ofsuitable circuitry from signals detected with the one or more sensors instep 1104 or may be digital signals directly sampled by the sensors instep 1104.

Next, at step 1108 a portion of the digital signal acquired in step 1106is selected that corresponds to a MICR symbol responsive to operation ofa processor. At step 1110 the peaks of the portion of magnetic signalselected in step 1108 are determined. The peak weights are determined instep 1112. The peak weights may be determined through operation of aprocessor by any of the earlier discussed methods of calculating peakweight. In some example embodiments once the peak weights aredetermined, the weight values may be used by the processor operating inaccordance with its programming to determine which peaks qualify asvalid peaks, and only valid peaks may be used in any furtherdeterminations. At step 1114 the processor accesses data in a data storecomprising a predetermined set of data values for each of the pluralityof MICR symbols. In some example embodiments the sets of data values maycorrespond to peak amplitude values and in other example embodiments maycorrespond to peak weight values. In some example embodiments the setsof data values may correspond to feature vector element values. The datarepresenting the magnetic signal portion may be compared throughoperation of a processor for correlation with each of the predeterminedsets of data accessed in step 1116 and a determination may be made instep 1118 as to which MICR symbol the magnetic signal portioncorresponds. The determination in step 1118 may correspond to how wellthe sample data values correlate to the predetermined sets of datavalues accessed in step 1114. The determination of the type of sensedMICR symbol is made by the processor based on the sample data valuesgenerally corresponding to one of the sets of predetermined data valuesfor the type of MICR symbol.

In some embodiments weight of a peak may be sensitive to baseline shiftin the sensed data. As graphically represented, a cut related to thearea enclosed by a curve and the cord that “cuts” across the base of thepeak may be generally independent of baseline shift. In some exampleembodiments a cut function may be calculated through operation of aprocessor which corresponds to a modified area under the waveform curve.In some example embodiments the cut may be used to more accuratelydistinguish smaller true peaks from peaks produced by baseline drift. Anexample of a cut function c(x) is shown in Equation 18.c(x)=w(x)−h[f(x−h)+f(x+h)]  EQ 18The shaded areas shown in FIGS. 27 and 32 show examples of the cut areasc(x) or modified weights determined for the peaks 722, 724. In anexample embodiment, cut area values above a predetermined threshold maybe used by a processor to make a determination as to which peaks in adetected waveform curve are to be used to form a feature vector for theMICR symbol. In some example embodiments the cut of a peak and theweight of a peak may be both calculated, and both may be factored into acalculation made by a processor in determining if the peak is a validpeak to use to form an element in a feature vector.

In some example embodiments the cut series may be useful in determiningwhether a sensed peak is valid or invalid. The cut series may be definedto be the difference between the area under the curve around a peak andthe product of the average of the endpoints and distance from the peakcenter to the endpoints. If the sum series, s_(i), is defined as inEquation 19 as being the sum of the sub series of {y} from the firstindex j up to i, then the cut series may be defined as in

$\begin{matrix}{s_{i} = {\sum\limits_{j = 0}^{i}y_{j}}} & {{EQ}\mspace{14mu} 19}\end{matrix}$Equation 20 (where L=2*hw+1) or alternatively as in Equation 21 where hwis again the half width of the window or the distance from a peak centerto the distance hw on either side of the peak center. In some exampleembodiments when a peak is sampled eleven times (at five locations oneach side of peak) and once at the peak center, L=11 and hw=5.

$\begin{matrix}{c_{i} = {s_{i + {hw}} - s_{i - {hw} - 1} - {( {y_{i - {hw}} + y_{i + {hw}}} ) \cdot ( \frac{L}{2} )}}} & {{EQ}\mspace{14mu} 20}\end{matrix}$c _(i) =s _(i−hw−1)−(y _(i−hw) +y _(i+hw))(hw+0.5)  EQ 21

In some example embodiments it may be useful to determine the anchordepth of a peak before making the calculation to determine which peaksare valid peaks. The anchor depth is related to how deep rooted (oranchored) the peak is. In some example embodiments the anchor depth maybe determined through operation of a processor by determining themagnitude of the value of a magnetic waveform at distance hw on eachside of the center of the peak, wherein the anchor depth is defined asthe smaller magnitude of the two magnetic values at a distance hw oneach side of the center of the peak. For example for hw=5, then ify(i−5)=−12 and y(i+5)=−10, then the anchor depth will be 10, which isthe lesser of the two amplitudes.

An example embodiment of using peak anchor depths, peak cuts, peak cutseries, weights and peak amplitude values to detect MICR symbols from amagnetic waveform is schematically shown by the method steps in FIG. 36.The method is carried out by an ATM responsive to computer executableinstructions carried out by at least one processor. The method begins atstep 1202 where a check is received at an ATM. In some exampleembodiments documents that contain MICR symbols other than a check maybe received. Next at step 1204 the check is transported into the ATM byone or more suitable transport devices. While the check is transportedit may be moved adjacent to one or more sensors such a read headscapable of detecting magnetic ink. At step 1206 magnetic signals areacquired from the check. At step 1208 a portion of the magnetic signalsacquired in step 1206 is selected through operation of the processor,that corresponds to a MICR symbol. In some example embodiments thesignal portion may not actually correspond well with stored data for anyMICR symbol, and when an analysis is performed in a later step it may bedetected that the signal portion does not correspond with a MICR symbol.When this occurs, the processor may operate in accordance with itsprogramming to collect other signal portions to determine if suchportions correspond to valid characters. As discussed previously thedetermination as to the bounds of magnetic characters may be determinedthrough operation of the processor based on signal gaps, signalvariations, signal fluctuations or other features or combinations ofsignal patterns that generally correspond to the separation ofcharacters for the particular types of characters being analyzed.

At step 1210 the possible peaks of the signal portion are determined.The peaks are determined by a processor analyzing the magnetic waveformvalues and/or amplitudes. In some example embodiments the peaks may beanalyzed at eight fixed or processor resolved locations relative to theMICR symbol. In some example embodiments when a peak is determinedthrough operation of the processor with a high confidence, that peak maybe labeled valid. Other peak values may be determined at fixed locationsaway from a peak location that have been determined proper peaks foranalysis based on operation of the processor. In yet other exampleembodiments other peaks may be searched for through operation of theprocessor in a small range of areas where each small range of areas is afixed distance from a valid or a high confidence peak. Of course theseapproaches are exemplary.

At step 1212 data corresponding to a zone around each peak is selectedthrough operation of a processor. In some example embodiments the zonelength will be the same distance on each side of a peak center so thatthe peak is centered in the zone. In some example embodiments the zonewill correspond with a fixed number of magnetic waveform samplelocations within a zone. In some example embodiments the zone samplelocations are an equal distance apart. At steps 1214 through 1220 theanchor depth, weight, peak cut and cut series respectively arecalculated for each peak through operation of the processor. In step1222 a determination is made as to which peaks are valid. Thedetermination may be based on comparison of one or more features of thepeak to one or more values and/or thresholds including the amplitude,anchor depth, weight, peak cut and cut series of the peak. The magneticsignal data resolved may be analyzed for correlation with apredetermined set of MICR symbols in step 1224. In some exampleembodiments the correlation may be based on correlating the peaks of themagnetic signal portion comprising a sample feature vector to a set ofpredetermined symbol feature vectors that correspond with one or morepredetermined anchor depth, weight, peak cut or the cut series of eachsymbol of a given MICR character set. In some example embodiments thecorrelation function may be a Pearson correlation or any other suitablecorrelation technique. At step 1226 a determination is made throughoperation of the processor as to which MICR symbol the signal portioncorresponds.

As discussed previously, in an example embodiment a comparisoncalculation may be performed between the determined sample featurevector of a detected magnetic waveform, and each of the plurality ofpredetermined symbol feature vectors for each of the standard E-13b MICRsymbols or the standard CMC-7 symbols. In an example embodiment aPearson correlation may be used to produce a correlation coefficientwhich is a quantity that gives the quality of a least square fitting tothe original data. A higher Pearson correlation coefficient indicates ahigher correlation between data sets, while a relatively lower Pearsoncorrelation coefficient indicates a lower correlation between data sets.In an example embodiment, a correlation value of 1.0 corresponds to anexact match between the detected sample feature vector and thepredetermined symbol feature vector of a standard E-13b symbol. Inpractice an exact match may be rare; however, as discussed previously,correlation coefficients which are closer to a value of 1.0 correspondto a relatively higher correlation between data sets than correlationcoefficients that are relatively smaller in value. Thus, of the fourteenstandard E-13b symbols the detected magnetic waveform is being analyzedfor correlation to the symbol which produces the highest correlationcoefficient with respect to the detected symbol may be determined by aprocessor as the correct match for the detected symbol. A similardetermination may be made for a magnetic waveform being correlated witha feature vector representing distances between peaks and the fifteenstandard CMC-7 symbol feature vectors of FIG. 31.

Using the E-13b MICR as an example, the Pearson correlation r_(xy)between two vectors {right arrow over (x)} and {right arrow over (y)}may be calculated according to Equation 21. Equation 21 is an innerproduct of the magnetic waveform feature vector and one of thepredetermined MICR symbol feature vectors.

$\begin{matrix}{r_{xy} = {{\overset{harpoonup}{x} \otimes \overset{harpoonup}{y}} = {{\overset{harpoonup}{y} \otimes \overset{harpoonup}{x}} = \frac{s_{xy}}{\sqrt{s_{xx}s_{yy}}}}}} & {{EQ}\mspace{14mu} 21}\end{matrix}$Where s_(xx), s_(yy) and s_(xy) in Equation 21 are defined by Equations22 to 24. In some example embodiments for E-13b MICR feature vectorscorresponding to eight possible peak positions, the value of n is eight.In other example embodiments for CMC-7 MICR feature vectorscorresponding to six distances between peaks, the value of n is six.

$\begin{matrix}{s_{xx} = {{\sum\limits_{i = 1}^{n}( {x_{i} - u_{x}} )^{2}} = {( {\sum\limits_{i - 1}^{n}x_{i}^{2}} ) - {nu}_{x}^{2}}}} & {{EQ}\mspace{14mu} 22}\end{matrix}$

$\begin{matrix}{s_{yy} = {{\sum\limits_{i = 1}^{n}( {y_{i} - u_{y}} )^{2}} = {( {\sum\limits_{i - 1}^{n}y_{i}^{2}} ) - {nu}_{y}^{2}}}} & {{EQ}\mspace{14mu} 23}\end{matrix}$

$\begin{matrix}{s_{xy} = {{\sum\limits_{i = 1}^{n}{( {x_{i} - u_{x}} )( {y_{i} - u_{y}} )}} = {( {\sum\limits_{i = 1}^{n}{x_{i}y_{i}}} ) - {{nu}_{x}u_{y}}}}} & {{EQ}\mspace{14mu} 24}\end{matrix}$

A standard cross-correlation matrix may be calculated that determineshow well each of the standard predetermined feature vectors correlatewith each of the other predetermined feature vectors. The standardcross-correlation matrix is shown in FIG. 38 and may be defined forE-13b as a 14-by-14 matrix. The matrix elements V_(ij) may be the vectorcorrelation coefficient between feature {right arrow over (v)}_(y)vector and {right arrow over (v)}_(i). FIG. 38 is produced with vector{right arrow over (v)}₀ corresponding to the symbol 0, . . . , and{right arrow over (v)}₉ symbol 9, {right arrow over (v)}₁₀ the transitsymbol (T), {right arrow over (v)}₁₁ the amount symbol (A), {right arrowover (v)}₁₂ the on-us symbol (U) and {right arrow over (v)}₁₃ the dashsymbol (D). The standard feature vector table may also then be expressedas the matrix V=[{right arrow over (v)}₀ {right arrow over (v)}₁ {rightarrow over (v)}₂ {right arrow over (v)}₃ {right arrow over (v)}₄ {rightarrow over (v)}₅ {right arrow over (v)}₆ {right arrow over (v)}₇ {rightarrow over (v)}₈ {right arrow over (v)}₉ {right arrow over (v)}₁₀ {rightarrow over (v)}₁₁ {right arrow over (v)}₁₂ {right arrow over (v)}₁₃]which represents the matrix of FIG. 38. Matrix V is a symmetric matrixwith V_(ij)=V_(ji). Each element along the diagonal line is one becausethat line represents when standard predetermined feature vector has beencorrelated to itself which represents a 100% confidence in thatcorrelation. In some exemplary embodiments should be one or as close toone as possible for any i≠j. For a given magnetic waveform symbol to berecognized, in some embodiments the feature vector {right arrow over(v)}_(x) may be constructed having elements corresponding to peakamplitudes, weights or any other suitable parameter such as distancesbetween peaks for CMC-7 font. For one example embodiment for the E-13bfont the feature vector will have element values corresponding to peakweights where the magnetic waveform feature vector may be represented as{right arrow over (v)}_(x)=({right arrow over (w)}₁ {right arrow over(w)}₂ {right arrow over (w)}₃ {right arrow over (w)}₄ {right arrow over(w)}₅ {right arrow over (w)}₆ {right arrow over (w)}₇ {right arrow over(w)}₈). The element weights may be weight values of a peak calculated asdiscussed earlier and may be values derived from the filtered magneticsignal {y} that was discussed earlier.

Symbol recognition of the symbol x is in some example embodimentsaccomplished by a processor operating in accordance with its programmingto calculate correlation coefficients between {right arrow over (v)}_(x)and all 14 standard E-13b feature vectors in matrix V, and producing a14-dimension result vector {right arrow over (r)}. The inner productoperator may be used to represent the result vector as Equation 25.{right arrow over (r)}=( {right arrow over (v)} _(x)

V)^(T)  EQ 25The result column vector {right arrow over (r)}=(r₀ r₁ r₂ r₃r₄ r₅ r₆ r₇r₈ r₉ r₁₀ r₁₁ r₁₂ r₁₃) may represent the inner product of {right arrowover (v)}_(x) with each of the predetermined standard feature vectors.The largest element r_(k) of vector {right arrow over (r)} may indicatethe recognized symbol which is the corresponding feature vector {rightarrow over (v)}_(x) in V. The value of r_(k) may also be referred to asthe confidence level. In general, in some example embodiments aconfidence level greater than or equal to 95% may indicate a positiveidentification while a confidence level less than 95% and above 90% maystill be acceptable. However, a confidence level under 90% may be takenas questionable. Of course these approaches are exemplary.

An example embodiment to detect MICR symbols by combining filtering andcorrelating a magnetic waveform is shown schematically through themethod steps 1300 represented in FIG. 39. The method is carried out byoperation of an ATM responsive to at least one processor carrying outcomputer executable instructions. The method begins at step 1302 where adocument with MICR symbols is received at an automatic banking machine.At step 1304 the document is moved across at least one magnetic sensorsuch as a read head. In some example embodiments the document is movedon a transport that operates at a transport speed of about 500 mm/s. Atstep 1306 magnetic signals are acquired by the read head from thedocument as it is moved across one or more read heads or other magneticsensors that are operative to sense the strength of magnetic propertiessensed at each location from the magnetic ink of the MICR symbols. Insome example embodiments the acquired signals may be raw signals thatare uncorrected and unfiltered. In other embodiments the signals may beconditioned and/or converted to digital signals by suitable circuitry.In some example embodiments the magnetic sensor may comprise a pluralityof sensor elements arranged consecutively along at least one column, andmay be operative to acquire magnetic signals from a plurality of thesensor elements as the check moves across the sensor. Of course thisapproach is exemplary.

In some example embodiments the signals corresponding to the MICRsymbols on the document may be sampled about every 63.5 micro-secondsand the sample values may be converted to an eight bit unsigned integervalue through operation of suitable circuitry. In some exampleembodiments the raw signal may be a baseline signal corrected at step1308. In some example embodiments the baseline correction may be carriedout through operation of a processor based on an average value that issubtracted from each raw digitized magnetic signal. In some exampleembodiments the average value may be the average value over acorresponding fixed range of raw digitized magnetic signals. In someexample embodiments when a new raw magnetic sample is acquired, it maybe added through operation of the processor to a fixed raw magneticsample range and the oldest raw magnetic sample from the fixed rawmagnetic sample range may be removed and the average value may berecalculated. At step 1310 the baseline corrected signal may be filteredby appropriate circuitry to boost the signal to noise ratio byattenuating high frequency noise. In some example embodiments thefiltering may be performed through operation of at least one processordigitally filtering the corrected magnetic signal. In some exampleembodiments the baseline corrected magnetic signals may be filtered witha Bessel IFF. In yet other example embodiments the Bessel IIF may be a10th order filter.

At step 1312 a portion of the filtered signal is evaluated throughoperation of a processor for purposes of determining if it correspondsto a MICR symbol. In some example embodiments this determination may bemade by locating a peak after a one-eighth inch quiet period of notdetecting any peaks between which correspond to an area MICR symbols. Atstep 1314 the data values corresponding to peaks of the magneticwaveform that corresponds to a MICR symbol may be determined. At step1316 the data values corresponding to peaks may be analyzed forcorrelation to data corresponding to each of the MICR symbols of a MICRfont. In some example embodiments a confidence level for eachcorrelation between the magnetic symbol peaks and each MICR symbol isdetermined through operation of a processor. In some example embodimentsthe correlation may be achieved using a Pearson correlation. In someexample embodiments when none of the confidence levels is determined tobe above a confidence threshold value, then a second magnetic signalportion that is offset from the initial magnetic signal portion by atleast one magnetic sample may be determined and steps 1314 and step 1316may be repeated with the second magnetic signal portion. At step 1318 adetermination is made through operation of a processor as to which MICRsymbol corresponds to the magnetic signal data. The steps may then berepeated for all of the magnetic signal data to resolve all of themagnetic symbols on the document. At step 1320 the document may bestored in the automatic banking machine or returned to the customer. Insome example embodiments at least one message may be sent to at leastone a remote computer, and the at least one message may include datarepresentative of data resolved from the MICR symbols of the document.At least one message from the remote computer may include data whichcauses the machine to accept and store the check or return it to acustomer. Of course this approach is merely exemplary.

In some example embodiments the ATM may be operative to read the MICRsymbols on the document in all four orientations as representedschematically by FIGS. 40-43. In some example embodiments an ATM mayread all four orientations with only one magnetic read head sensor atthe top of the document and one magnetic sensor at the bottom of thedocument. In some example embodiments when the check is upside down thedata received by the magnetic sensor corresponding to the side of thedocument with magnetic data may be filtered and amplified to allowbetter recovery of the peaks of the signal. The ATM may be operative toadjust the offset and the signal to noise ratio of the magnetic signalas discussed earlier. For example, features described in U.S. patentapplication Ser. No. 11/983,401 filed Nov. 8, 2007 may be used in someembodiments and the disclosure of this application is incorporatedherein by reference in its entirety.

In some example embodiments a camera or other imaging device such as alinear CCD array is operative to capture optical images of the documentmay be placed on both sides of the transport. One imaging device may beoperative to capture images of the front face of the document and oneimaging device may capture images of the rear face of a document. Insome example embodiments the imaging devices may be operative to captureimages of a document to provide image data to optical recognitionsoftware that is operative to assist in the detection of MICR symbols.In alternative embodiments contact image scanners may be used to captureimage data in a pixelated format.

In some example embodiments E-13b MICR symbols may be detected with anATM operative to determine if a valid MICR line is located at near the“top” or “bottom” of a document face no matter which way the documentsmay be oriented. In some example embodiments the ATM may be operative toread the top and bottom regions of a check with top and bottom magneticsensors. The top and bottom magnetic waveforms may both then be analyzedthrough operation of a processor as described previously to detect thepeaks that may correspond to MICR symbols. As before, data correspondingto the MICR peaks may be compared through operation of a processor forcorrelation to a predetermined data sets corresponding to MICR symbols.It should be understood that the “top” and “bottom” references are forconvenience only in describing representations of checks in the mannershown in the drawings and do not refer to relative vertical positions inan apparatus. In some example embodiments the correlation may be betweena sample feature vector corresponding to the sensed magnetic waveformand predetermined symbol feature vectors representing the predeterminedMICR symbols. Of course these approaches are exemplary.

In some example embodiments when the check is front face up, right sideup as in FIG. 40 the E-13b magnetic waveforms corresponding to each ofthe symbols as shown in FIG. 23 may each correspond well to theirassociated predetermined feature vector in FIG. 24. In some exampleembodiments if the MICR line on the check is scanned from left to rightthen the MICR symbols on the check are scanned from the front edge ofthe symbol to the back edge of a symbol. In some example embodiments theleft to right scanning may be accomplished by using a transport thatmoves the check from right to left under stationary sensors.Magnetically scanning from the front to back edge of a MICR symbol meansthe sample feature vector of the magnetic waveform should correspondwell to the feature vectors of FIG. 24. The reason is that the featurevectors of FIG. 24 may have been derived from scanning ideal MICRsymbols from left to right as shown in the waveforms of FIG. 23. Becausethe magnetic scan of the bottom magnetic sensor may correspond very wellwith predetermined symbol feature vectors, there should be a high numberof detected symbols so there may be a high confidence the MICR wascorrectly detected in the orientation represented in FIG. 40.

In contrast, in the face up, right side up orientation represented inFIG. 40, the upper magnetic scanner may not detect many magneticwaveforms that correspond to MICR symbols. In some example embodimentsbecause the MICR on a check is generally located at the “bottom,” thetop sensor has essentially no magnetic data to sense. Without valid MICRdata, the sensor may not detect any valid MICR symbols. In some exampleembodiments when the bottom sensor detects data corresponding to manymore valid MICR symbols than the top sensor, the top sensor data valuesmay be disregarded.

A corresponding orientation related to when the check may be in thefront face up, right side up position is when the check is front faceupside down right side up as represented in FIG. 42. In some exampleembodiments when the MICR symbols of a check are read with a check inthis orientation it is again scanned from left to right, it may still bepossible to detect valid MICR symbols. In this orientation the MICRsymbols will still be scanned from the front of the symbol to the rearof the symbol again. This means the sample feature vector derived fromthe waveform will correspond well with one of the predetermined featurevectors of FIG. 24. However, because the check is now “upside down” themagnetic signal will generally be weaker when it is read through thecheck. In some example embodiments an offset adjustment may be performedthrough operation of the processor to reposition the peaks for analysisas discussed above. Additionally, filtering may be performed on themagnetic waveforms to improve the signal to noise ratio as discussedabove to aid in detecting the MICR symbol to which a sensed waveform maycorrespond. Because the upper sensor is scanning in the direction whichcorresponds to the feature vectors, many of the MICR symbols may berecovered with a high confidence when the check is oriented asrepresented in FIG. 42. Because the bottom sensor is on the oppositeedge of the check from the MICR symbols, the bottom sensor may notdetect any valid MICR symbols and the bottom sensor data may bedisregarded. The processor may operate in accordance with itsprogramming in deciding when data may be discarded.

In some example embodiments four sensors may be used to read the MICRdata. Four sensors may allow both sides of the check to be read on thetop of the check and both sides of the check to be read at the bottom ofthe check. However, providing four sensors may be more costly in thatusing only two sensors may require twice the computing power to filter,sample and correlate waveforms and associated peaks. Of course theseapproaches are exemplary.

In some example embodiments a check may be oriented as represented inFIG. 41 with the front face up, upside down. In this orientation thecheck itself may still be scanned from left to right. However, now theE-13b MICR symbols are scanned from the back of a MICR symbol to thefront of a MICR symbol when the same transport is used to transport thecheck from right to left. The magnetic waveforms scanned from a check asoriented in FIG. 41 are in the opposite directions to those in FIG. 23.Because the waveforms are not symmetric in some embodiments the scannedmagnetic waveforms may not correlate well with the waveforms of FIG. 23because the waveforms in FIG. 23 correspond to MICR symbols scanned fromleft to right. Because the sampled magnetic waveforms may not correlatewell with the waveforms of FIG. 23, the magnetic waveform may notcorrespond well with any of the E-13b feature vectors in FIG. 24.

Even though none of the waveforms of FIG. 23 may be symmetric, invertedwaveforms and portions of some waveforms in FIG. 23 may be nearlysymmetric. The partial symmetry may be used to accurately resolve someof the MICR symbols in some example embodiments that have MICR symbolsscanned from back to front. For example the waveforms in FIG. 23 for theMICR symbols “0” and “8” are approximately symmetric if the peaks areinverted after scanning from back to front. In a front to back scan of aMICR symbol “0” there would be a detection of the four peaks shown inFIG. 23. At sample location one (using the exemplary eight positionsample locations discussed earlier and shown in FIG. 30) a positive peakmay be detected followed by a negative peak at location two. Samplelocations three through six would not detect any peaks. Sample locationseven would detect a positive peak followed by a negative peak. The peakamplitudes correspond to the feature vector for the symbol “0” in FIG.24. If the waveform for the MICR symbol for “0” in FIG. 23 is scanned inreverse (back to front), sample position one has a negative peak, sampleposition two has a positive peak, sample locations three through sixhave no peaks, sample location seven has a negative peak and samplelocation eight has a positive peak. If all the peak values in the backto front scan are inverted, then the back to front scan may roughlycorrespond to the front to back scan for the MICR symbol “0.” The frontto back feature vector of FIG. 24 and the back to front scan (withinverted amplitudes) feature vector for the MICR symbol “0” may be verysimilar.

Because the inverted feature vectors may be very similar, at least oneprocessor is programmed to correlate the MICR symbol “0” when it isscanned in the back to front direction. In some example embodiments thecorrelation matrix may have an additional vector for the MICR symbol “0”that has the values of the original feature vector of the MICR symbol“0” inverted. Because the magnetic waveform in FIG. 23 is symmetricalfor the MICR symbol “8” (with peaks inverted) a similar approach may insome example embodiments may be used to detect the symbol “8” when it isscanned in the back to front direction as in FIG. 23.

In some example embodiments all the waveforms of FIG. 23 are scanned inthe back to front direction represented in FIG. 41 and sampled at thesame eight corresponding sample locations. The sample locations maycorrespond to the sample locations of the elements in the featurevectors of FIG. 24. Fourteen new feature vectors may be added to FIG. 24that correspond to each of the MICR symbols of FIG. 24 being scanned inthe back to front direction.

In some example embodiments a portion of a MICR symbol may be symmetric.For the E-13b MICR symbol “2” the magnetic waveform of FIG. 23 issymmetric with respect to its first five sample locations correspondingto a positive peak, negative peak, no peak, positive peak and negativepeak read in the front to back scan. In reverse right to left scan, withthe peaks again inverted, the sequence may be similar over this portionof the magnetic waveform. The corresponding feature vector in FIG. 34for the E-13b MICR symbol “2” is symmetric when the first five elementsare considered by themselves. The magnetic waveform for the E-13b MICRsymbol “5” may be partially symmetric as may be the first six vectorelements (sample locations) of the corresponding feature vector in FIG.24 when the magnitudes are inverted.

In some example embodiments a processor operates to detect a portion ofthe feature vectors that may be symmetric (with inverted magnitudes)when reading MICR symbols from back to front. In some exampleembodiments E-13b MICR symbols may be scanned from back to front thenmay have their sample magnitudes inverted before correlation to thefeature vectors of FIG. 24. In some example embodiments the symmetricportion (with inverted magnitudes) of a sampled magnetic waveform mayneed to be shifted so that symmetric portion may correspond to a portionof a feature vector in FIG. 24. As shown in for MICR symbols “2” and “5”in FIG. 24 the symmetric portion may need to be shifted so that thefirst five vector elements correspond to the symmetric portion and thelast three correspond to zero. For the MICR symbol “5” the symmetricportion may need to be shifted so the last two elements correspond tozero.

In some example embodiments when a check is in the front face down,right side up orientation as represented in FIG. 42 the MICR symbols areagain read from back to front. Because the check is now upside down theMICR symbols may be read through the paper by the “bottom” sensor. Themagnetic waveform captured by the sensor may offset shifted and filteredto improve the signal to noise ratio as discussed earlier. In someexample embodiments when the signal has been recovered, some of theE-13b MICR symbols may be recovered as discussed above for the back tofront scan for the orientation represented in FIG. 41.

In some example embodiments when only some of the symbols are recoveredthrough one sensor as discussed above for the orientations representedFIGS. 41 and 42, data from the other magnetic sensor may not be analyzedto save processor power. Turning off one of the two magnetic sensors mayallow one or more processors to be fully operative to analyze thesignals for the magnetic sensor that has detected some MICR symbols. Insome example embodiments when only certain MICR symbols that may bepartially symmetric are detected, the feature vector table for front toback scans of FIG. 24 may be disabled and another feature vector tablecorresponding to back to front scans of MICR symbols may be used todetect the MICR symbols. In some example embodiments when some MICRsymbols are detected with symmetric properties, data corresponding tooptical images of unrecognized MICR symbols may be processed using atleast one processor operating optical image recognition software torecognize the optical MICR symbol images.

In some example embodiments CMC-7 MICR symbols may be read in any of thefour orientations represented in FIGS. 40-43. Detection of CMC-7 MICRsymbols in any orientation may be more difficult because the standardfeature vectors may all be symmetric with themselves or another featurevector. In the CMC-7 feature vector table of FIG. 31, the featurevectors for the symbols “0”, “8” and “I” are symmetric with themselves.They are symmetric whether read from front to back or from back tofront. For example the feature vector for the symbol “0” has the firsttwo and the last two elements for short peak distance values with thetwo middle element values for long distance values so the feature vectorfor “0” will appear similar whether the elements are read from first tolast or from last to first. The rest of the twelve feature vectors aresymmetric with one other feature vector. For example the feature vectorfor the CMC-7 MICR symbol “1” is symmetric to the feature vector for thesymbol “T.” The feature vector for the symbol “2” corresponds to “5”,“3” to “N”, “4” to “A”, “6” to “9” and “7” to “D.” Because there is acorresponding symmetric CMC-7 symbol for every CMC-7 symbol, it becomesmore difficult to recognize if the CMC-7 magnetic waveform is validlybeing read from front to back or from back to front. It is difficult todetermine which of the orientations shown in FIGS. 40-43 that the checkmay be in.

In some example embodiments optical symbol recognition functionality maybe combined with the magnetic MICR symbol recognition for the analysisof the CMC-7 MICR font. In some example embodiments when a CMC-7 MICRsymbol is detected, it may be correlated through operation of aprocessor with an optical symbol recognition result. This may be donefor the first detected symbol or the first several detected symbols.After the optical symbols are compared with the resolved MICR symbols itis possible to know the orientation of the MICR symbols. In some exampleembodiments once it is known which sensor is detecting valid magneticsymbols, the other sensor values may be disregarded or the sensor may beturned off and computing resources may be applied to data from thesensor detecting valid symbols. In some example embodiments once it isknown on which check face the MICR symbols are located and whether thesymbols are being read font to back or back to front the optical imagingfunction carried out through operation of a processor may be suspendedor turned off and the CMC-7 MICR symbols may be determined using onlymagnetic detection methods. Of course these approaches are exemplary.

An example embodiment operative to detect E-13b MICR symbols in any ofthe four possible check orientations is represented schematically as amethod 1400 in FIG. 44. Similar to other methods previously describedthe method begins at step 1402 where a document with MICR symbols isreceived at an automatic banking machine. In some embodiments thedocument is a banking check. At step 1404 the document is moved across“top” and “bottom” magnetic sensors. In some example embodiments the topmagnetic sensor may be operative to be positioned adjacent the top ofthe check as the check is moved in a transport. In some exampleembodiments the top and bottom sensors may both be operative to readmagnetic data from the face of the check facing magnetic sensor, ormagnetic data on the rear side of the check facing away from the topmagnetic sensor. At step 1406 magnetic signals are acquired from the topand bottom magnetic sensors as the check moves past the magneticsensors. At step 1408 magnetic signal regions corresponding to MICRsymbols are determined through operation of at least one processor. Itshould be understood that “top” and “bottom” refer to areas adjacentopposed the long side edges of the check and are used herein to refer tothe exemplary graphic representations in the drawings. There is norequirement that one area of a check be positioned vertically higherthan another area in carrying out the check analysis processes describedin this application. At step 1410 the magnetic signal regions arecorrelated to MICR symbols through operation of a processor. Adetermination of how many valid and invalid MICR symbols are detectedwith the top magnetic sensor and how many valid and invalid MICR symbolsare detected with the bottom magnetic sensor is made in steps 1412 and1414. The determination in steps 1412 and 1414 in the exemplaryembodiment is made using a sample feature vector analysis of themagnetic waveform based on a comparison to data corresponding to astandard set of predetermined symbol feature vectors similar to those inFIG. 24. In step 1416, responsive to step 1412 and 1414 it is determinedwhether the top or bottom magnetic sensors detected valid MICR symbols.

An example embodiment to detect CMC-7 MICR symbols in any of the fourpossible check orientations is shown schematically as method steps 1500in FIG. 45. The method begins at step 1502 where a document with MICRsymbols is received in an automatic banking machine. At step 1504 thedocument is moved across top and a bottom magnetic sensors. At step 1506magnetic signals are acquired from the top and bottom magnetic sensorsas the check moves past the magnetic sensors. At step 1508 magneticsignal regions corresponding to MICR symbols are determined throughoperation of a processor. At step 1510 an optical image is captured fromboth faces of the document. At step 1512 a determination is made as towhich MICR symbols the magnetic signal region correlates. At step 1514 adetermination is made through operation of a processor as to which MICRsymbol the optical image of at least one MICR symbol corresponds.Responsive to steps 1512 and 1514, a determination is made as to whetherthe top or bottom magnetic sensor detected valid MICR symbols.

In some example embodiments optical character recognition (OCR) andmagnetic symbol recognition techniques may be utilized in combination toimprove MICR symbol detection results. In some example embodiments allthe MICR symbols may be recognized using any of the earlier discussedtechniques and all of the MICR symbols may be detected by OCRtechniques. In some example embodiments the magnetic and OCR recognitionresults may be compared through operation of a processor for eachsymbol, position by position, to determine if both results agree. Insome example embodiments if the results disagree, whether the magneticor optical symbol will be associated with that position, may be resolvedbased on which MICR symbol and the magnetic technique associated withthe symbol position. For example if the magnetic technique associated a“2” or “5” symbol to the character, and the optical results disagree,then because of the “2 or 5” dichotomy discussed below the opticalresult may be used. Of course this approach is exemplary.

In some example embodiments combining optical and magnetic recognitionresults may be useful to resolve the “2 or 5” dichotomy. For example insome example embodiments if the magnetic symbol recognition techniquecarried out through operation of a processor predicts the symbol is a“2” or a “5,” then the resolved optical results may be assigned to thatsymbol position. The reason for favoring the optical results if thesignal is a “2” or “5” is because it may often be difficult todistinguish a “2” from a “5” using magnetic symbol recognitiontechniques. In FIG. 23 the magnetic waveform for a “2” looks like thewaveform for a “5” with the main difference being that a “5” has alarger center gap between peaks. Sometimes when a check is being movedby a transport past the magnetic sensors, the transport may slip and/orbecause of electrical noise, the space between peaks may be changed.When the center distance in a “5” shrinks it may look like a “2” andwhen the center distance in a “2” increases is may look like a “5.” Forother symbols that may be easier to detect magnetically than optically,the magnetic symbol may be used when a conflict with optical symbolrecognition results is detected. Various approaches may be taken andcarried out through processors executing the suitable program steps.

In some example embodiments a confidence level may be resolved throughoperation of a processor for each symbol that is magnetically recognizedand an optical confidence level may be resolved for each symboloptically recognized. In some example embodiments the magnetic andoptical confidence levels may be compared when there is a disagreementwith regard to the detected symbol. In some example embodiments thehighest confidence level may be selected. In some example embodimentswhen the magnetic confidence level is low, the data corresponding to themagnetic waveform sampled may be shifted and the magnetic symboldetection algorithm may be repeated to see if a better confidence leveland correlation to the optical results may be achieved.

In some example embodiments an initial optical image of the entire checkmay be captured. Next the MICR symbols may be located and cropped fromthat image data. After the MICR symbols are cropped this image of theMICR symbols may be contrast boosted. In some example embodiments thecontrast boosted image data may be de-skewed. The de-skewed image may beused with the OCR algorithm executed by a processor to opticallyrecognize the MICR symbols.

Some example embodiments may operate to more accurately position E-13bpeaks. In some example embodiments the data corresponding to peaks ofthe magnetic waveforms in FIG. 23 may correspond to eight fixed peaksample locations. The fixed peak sample locations may be equally spacedapart. As discussed above, the eight fixed peak sample locations maycorrespond to the eight elements in a feature vector of a symbol as inFIG. 24.

In some example embodiments when a peak is detected between samplelocations it may be flagged through operation of a processor. Thisflagging technique may also be very useful in resolving the “2 or 5”dichotomy when a transport slips. For example if the third peak in FIG.30 was between sample locations 3 and 4 at position 3.5, it would beflagged. In some sample embodiments when peak is flagged the processormanipulates the data so the peak will be moved to an adjacent sampleposition and a first E-13b feature vector for that waveform may begenerated through operation of the processor with a peak at thatlocation. In some example embodiments a second feature vector may becreated with the flagged peak in the position of the other adjacentlocation. For example with the third peak of FIG. 30 at position 3.5 afirst feature vector will be generated with the flagged peak at position3 and a second feature vector will be generated with the peak atposition 4. In some example embodiments data corresponding to both ofthese feature vectors may be correlated through operation of a processorto determine to which standard feature vector (corresponding to a knownsymbol) the sampled feature vector best correlated. The best of the twocorrelations may be selected and the other feature vector disregarded.In some example embodiments when a peak already exists at one adjacentlocation then only one feature vector may be created by moving a flaggedpeak to the other adjacent location.

In some example embodiments more than one peak of an E-13b MICR symbolwaveform may be flagged through operation of a processor. For examplepeaks may be flagged when they may be detected at position 2.4 and atposition 5.6. In this example case a feature vector may be generated byat least one processor manipulating the data in a way that correspondsto moving the first flagged peak to position 2 and the second flaggedpeak to position 5. A second feature vector may be created with thefirst flagged peak at position 3 and the second flagged peak at position5. A third and fourth feature vector may be created with the secondflagged feature vector at position 6 and the first flagged peak atpositions 2 and 3. The four feature vectors for the flagged peaks maynow be correlated through operation of a processor to the standardfeature vectors of FIG. 24 to determine which one is the bestcorrelation.

In some example embodiments an E-13b sample feature vector may begenerated that does not correspond well with any standard featurevector. In some example embodiments when the correlation is poor datacorresponding to a first peak may have been missed and caused poorcorrelation. In some example embodiments when the correlation is poor aleading peak may be inserted at E-13b position 1 (when using the eightposition feature vector discussed above). In some example embodimentsthe magnitude of the feature vector inserted at position 1 may be anaverage of all possible position 1 valid peak magnitudes. The featurevector may now be correlated through operation of a processor todetermine if there is now a good correlation with a standard E-13bfeature vector.

An example embodiment to detect E-13b MICR symbols using the flagging ofpeaks is represented by steps of a method 1600 in FIG. 46. The methodbegins at step 1602 where a document with MICR symbols is received in anautomatic banking machine. At step 1604 the document is moved across atleast one magnetic sensor. In step 1606 magnetic samples are sampledfrom sensor output through operation of circuitry including a processoras the sensor senses the document. In step 1608 it is determined throughoperation of the processor which magnetic signals correspond with MICRsymbols. At step 1610 data values corresponding to magnetic waveformMICR symbol peak values are generated. In step 1612 the peak data valuesare used by a processor to generate sample feature vector element valuesand generate at least one sample feature vector. If a peak location isin between two feature vector locations then the peak may be flagged andassociated through operation of a processor with an additional featurevectors in this step. At step 1614 the sample feature vector is comparedto each of a plurality of predetermined symbol feature vectors of a MICRfont. At step 1616 a determination is made through operation of aprocessor as to which MICR feature vector the sample feature vectorgenerally corresponds. If a peak data value may result in more than onefeature vector in step 1612, then in step 1618 a determination is madeby the processor as to which feature vector is likely the valid samplefeature vector.

In some example embodiments data corresponding to missing peaks may beadded or extra peaks may be removed through operation of the processorwhen detecting CMC-7 MICR peaks. As discussed earlier and shown in theexample CMC-7 waveform in FIG. 31, the standard CMC-7 feature vectorscorrespond to distance between magnetic CMC-7 MICR waveform peaks. Insome example embodiments the distances between peaks may be one of shortor long distances. The feature vectors of FIG. 31 all may have only twolong and four distances represented in the elements of a CMC-7 MICRsymbol feature vector. Because every CMC-7 feature vector waveform hasthe distance between peaks, there should be seven peaks detected forevery CMC-7 MICR symbol.

In some example embodiments when three short peaks should be detectedbut only two short peaks are detected, data corresponding to a peak maybe inserted through operation of the processor in between the longest ofthe long distances between peaks. The resulting waveform may now havethe required two long and four short peak distances for a valid CMC-7symbol. In some example embodiments when four long peaks are detectedbut zero short peaks are detected, data corresponding to a peak may beinserted in between each of the two longest of the long distancesbetween peaks. In some example embodiments when a peak is missing, athreshold of a peak magnitude may be used to detect valid peaks may belowered through the processor operating to change threshold values inaccordance with its programming. Lowering the peak threshold may resultin a missing peak being detected. Of course these approaches areexemplary.

In some example embodiments data may be processed such that CMC-7 MICRsymbol waveform peaks are removed through operation of the processorwhen more than seven peaks are detected. In some example embodimentswhen one long distance between peaks and six short distances betweenpeaks is detected, data corresponding to the peak between the twocorresponding shortest distances may be removed. In some exampleembodiments when an extra peak is detected, the distance to the nextpeak on both sides of each peak may be calculated and data correspondingto the peak with the shortest distances to both adjacent peaks may beremoved. In some example embodiments when an extra peak is detected andtwo adjacent peaks have a high weight and are located very near to oneanother with regard to distance transversely across the symbol, datacorresponding to one of those peaks may be removed. The programmingassociated with the processor is operative to carry out manipulation ofthe data to accomplish such analysis.

In some example embodiments when data corresponding to a first peak isremoved, the resulting CMC-7 feature vector may be cross correlated withthe standard predetermined symbol feature vectors of FIG. 31. If aftercomparison for correlation the confidence level is low, the processormay operate to modify the data such that the removed first peak isreinserted and a different second peak is removed. The feature vectorthat corresponds to the second peak being removed may be analyzedthrough operation of the processor and compared for correlation withdata for known symbols to see if the first or second feature vectorresults in a better correlation.

In some example embodiments data corresponding to the distances betweenpeaks may be calculated and compared to the standard feature vectorvalues for short distances (10) and the standard feature vectors forlong distances (15) between peaks to determine which peak to remove orwhere to insert a missing peak. For example if there is an extra peakand sequential relative distances of 15, 15, 10, 10, 10, 2, and 8 arebetween peaks, then the data corresponding to the peak between thedistances of 2 and 8 will be removed leaving a feature vectorcorresponding to the sequential consists of relative distances of 15,15, 10, 10, 10, 10.

In another example if data corresponding to sequential relativedistances of 15, 9, 6, 10, 10, 10, 10 are detected, there must be anextra peak because seven distances were detected. For CMC-7 there mustalways be four short distances and here they may already be detectedbecause the four distances of 10 correspond to the feature vector shortdistances in FIG. 31. Additionally, the first value of 15 correspondswell with the long values of FIG. 31. In some example embodiments atleast one processor may be operative to calculate and detect that if thepeak between the values 9 and 6 is removed, then a second long value of15 will result, so that peak may be the one removed.

In another example the relative sequential distances between peaks of30, 10, 10, 10, 10 are detected, so a peak must be missing. In thiscase, the processor may be operative to calculate and detect that if apeak is inserted in the middle of the distance 30, then the result mayproduce the two missing long distances each equal to 15.

In another example the sequential relative distances between peaks of15, 25, 10, 10, 10 are detected so a peak must be missing. In this caseat least one processor may be operative to calculate and detect that ifa peak is inserted at a location within the 25 distance, then the resultmay produce the missing long distance and the missing short distance. Inthis example it is determined by the processor that it may not be bestto place the missing peak in the middle of the distance of 25 becausethe sequence of distances 15, 12.5, 12.5, 10, 10, 10 may be the result.It may be better to generate a possible vector of 15, 15, 10, 10, 10,10, and a second possible vector of 15, 10, 15, 10, 10, 10 and thencompare these vectors with a symbol resolved another recognitiontechnique such as image analysis to determine the correct CMC-7 MICRsymbol.

In some example embodiments the CMC-7 feature vector resulting frompeaks being removed or inserted may not correlate well with any of thestandard feature vectors of FIG. 31. In that case, the processoroperates so that data corresponding to a removed peak is reinserted oran added peak may be removed and a different peak is removed orinserted. The new sample feature vector may be re-correlated todetermine if the new feature vector has a better correspondence.

An example embodiment to determine E-13b MICR symbols using the flaggingof distances between peaks is represented schematically as a method 1700in FIG. 47. The method begins at step 1702 where a document with MICRsymbols is received at an automatic banking machine. At step 1704 thedocument is moved across at least one magnetic sensor. In step 1706magnetic samples corresponding to signals from the sensor are sampledfrom the document. In step 1708 it is determined through operation of aprocessor which magnetic signals correspond with MICR symbols. At step1710 data corresponding to relative distances between magnetic waveformMICR symbol peaks are generated. At step 1712 peak distances are used togenerate a sample feature vector. In this step if a peak is missing orthere is an extra peak, then more than one feature vector may begenerated. At step 1714 the sample feature vector may be analyzed forcorrelation to each of a plurality of predetermined symbol featurevectors. In step 1716 it is determined to which MICR symbol the samplefeature vector generally corresponds. At step 1718, in the case whenmore than one sample feature vector was generated in step 1712, adetermination is made as to which sample feature vector thecorresponding MICR symbol is most likely correct. OCR results determinedthrough operation of a processor may be used in this step to verify thecorrect MICR symbol.

In some example embodiments optical scan lines may be used to locate theMICR line on a document or a check. In some example embodiments once theposition of a MICR symbol is detected using scan lines, datacorresponding to the location of the symbol may be given to an OCRsoftware application operating in a processor. FIG. 48 graphicallyillustrates data analysis carried out by a processor corresponding tothe use of optical scan lines to locate MICR symbols 1860, 1862, 1863,1866. It should be understood that the references to horizontally andvertically refer to the corresponding representation in the drawingsonly. In some example embodiments the check 1810 is horizontally scannedas shown by example scan lines 1830 and 1832. In some exampleembodiments image data corresponding to the MICR line 1820 may beoptically cropped from the check. In some example embodiments data fromany number of suitable scan lines may be captured and the number of scanlines may depend on the type of optical scanner used. As the check 1810is scanned horizontally in the graphical representation shown, datacorresponding to vertical graph 1850 corresponding to the intensity ofthe image being scanned may be produced based on pixel values throughoperation of the processor. In some example embodiments the MICR symbols1860, 1862, 1863, 1866 on a check may be at least a 30% darker contrastthan the other visible images on the check. Because the MICR symbols maybe darker than the rest of the check, data corresponding to a verticalgraph 1850 may be produced through operation of the processor that isoperative to allow the spatial location of the MICR symbols 1860, 1862,1863, 1866 to be determined. The “vertical” graph may in someembodiments comprise a waveform representing optical image density of ascan line corresponding to the vertical location of the scan line on thecheck 1810. The location may be determined by the processor detectingwhere the vertical graph 1850 optical density is higher than the rest ofthe optical intensity of the rest of the check. This is based on thepixel values corresponding to darkness of the printed MICR symbols. Insome example embodiments the vertical location of the MICR line 1820 maynow be presented to an OCR software application.

In some example embodiments the image data corresponding to the MICRline 1820 may now be horizontally cropped. In some example embodimentsthe data corresponding to MICR line 1820 may now be contrast boosted. Insome example embodiments the MICR line 1820 may now be opticallyvertically scanned 1870 to determine the positions of the individualMICR symbols 1860, 1862, 1863, 1866. Data corresponding to a horizontalgraph 1880 may be produced and the processor is operative to cause thehorizontal locations of the MICR symbols 1860, 1862, 1863, 1866 to belocated. The horizontal graph may be a waveform representing opticalimage density of a vertical scan line corresponding to horizontallocation of the scan line on the check 1810. The symbol locations may bedetermined through operation of a processor by detecting where thehorizontal graph 1880 optical density (darkness of pixels) is greaterthan the rest of the optical intensity of the rest of the check. In someexample embodiments data corresponding to the horizontal location of theMICR symbols 1860, 1862, 1863, 1866 may now be input to an OCR softwareapplication operating in a processor. In some example embodiments about11 vertical scans of each MICR symbol may be performed responsive tooperation of a processor to accurately detect the horizontal location ofa MICR symbols 1860, 1862, 1863, 1866. In some example embodiments theuse of optical scanning to locate a MICR symbol may reduce computationalresources as compared to having traditional OCR software perform boththe MICR symbol location and the MICR symbol recognition. Of coursethese approaches are exemplary.

In some example embodiments it may be possible to use a single linealsensor array optical scan to determine to what MICR symbol the opticalscan image data corresponds. FIG. 49 is a graphical representation ofwhat the optical density data may look like for each if the E-13b MICRsymbols when the symbols are scanned with a contact image sensorcomprising a linear sensing array from right to left. In some exampleembodiments the waveforms of FIG. 49 may correlate to the derivatives ofthe magnetic waveforms of FIG. 23.

In some example embodiments the waveforms of FIG. 49 may be sampledthrough operation of a processor to produce an optical feature vectorfor each if the E-13b MICR symbols as was done in FIG. 23 to produce thestandard magnetic feature vectors of FIG. 24. After a standard opticalfeature vector table is derived and stored in a data store, then theE-13b MICR symbols may be optically scanned and an optical samplefeature vector generated. The optical sample feature vector may then becompared through operation of a processor for correlation to data forthe plurality of predetermined standard optical symbol feature vectorsto determine to which symbol the optical sample feature vector maycorrespond. In some example embodiments the optical signal may besampled in the eight standard locations as discussed earlier and anoptical sample feature vector with eight elements may be generated. Theeight feature vector elements may correspond to the amplitude of thewaveforms in FIG. 49 at eight fixed locations. The earlier discussedsampling, filtering and correlating techniques described for magneticMICR waveforms may be applied in whole or in part to the opticalintensity MICR waveforms. Of course these approaches are exemplary andin other embodiments other approaches may be used.

It is to be understood, that although a Pearson correlation has beendescribed in the above example embodiments, in alternative exampleembodiments, other types of correlation calculations may be carried outthrough operation of at least one processor to determine which of thefourteen standard E-13b symbols or the fifteen standard CMC-7 symbolsmost closely matches a MICR line symbol on a check. Further, althoughthe above described example method discusses symbols on a check in theE-13b font, in other example embodiments, the above described detectionmethod may be used to magnetically detect symbols printed on checks inother types of fonts in the MICR line or elsewhere.

Also, in other example embodiments, different MICR fonts may be detectedusing different circuits connected in parallel to the magnetic sensor.Each of the different circuits may be tuned to more accurately capturemagnetic waveforms which properly distinguish the symbols in thedifferent respective MICR fonts.

FIG. 29 shows an example of a three-dimensional graphical representation900 of magnetic patterns on a check as detected by the reader head in anexample embodiment of a check processing device. The grayscale features902 projecting from a surface 904 representation of the check correspondto the levels of the magnetic signals detected at the correspondinglocations on the check. The vertically higher the grayscale feature, therelatively stronger the magnetic signal for that location. The absenceof grayscale features on portions 906 of the check indicates thatmagnetic signals were either not detected or were below a minimumthreshold for those portions on the check. In an exemplary embodiment adiagnostic software application is operative to generate datacorresponding to such a three-dimensional graphical representation ofthe magnetic patterns on documents responsive to magnetic scans producedby the check processing device. Such graphical representations 900produced by the software for a given document may be output through adisplay on the ATM and used to aid a user in identifying magneticfeatures useful for identifying the type or other characteristics of thedocument. Information about the identified magnetic features may then beincorporated into the programming of the ATM.

For example, an embodiment may carry out a method of generating datacorresponding to such three dimensional graphs through operation of acomputer and displaying such graphs through a display device. The datacorresponding to such graphs may be generated from magnetic scansdirectly received from an operating check processing device, or thegraphs may be generated from magnetic scans previously generated andstored in a data store in the ATM.

In this described example embodiment, the method may also includeprogramming corresponding to identifying two-dimensional areas or zoneson the check which may and/or may not be associated with magneticsignals of particular levels. The method may also include storingthrough operation of a processor the data corresponding to theidentified areas and levels in a data store in operative connection withthe ATM. The method may also include configuring and/or programming theATM so that the processor in is responsive to the stored data whenevaluating the processed documents.

Computer executable software instructions used in operating theautomated banking machines and connected computers, and suchinstructions may be resident on and/or loaded from computer readablemedia or articles of various types into the respective processors. Suchcomputer executable software instructions may be included on and loadedfrom one or more articles such as diskettes, compact disks, CDs, DVDs,tapes, flash memory device, hard drives, RAM, ROM and/or other internalor portable storage devices placed in operative connection with theautomated banking machine. Other articles which include datarepresentative of the instructions for operating computers in the mannerdescribed herein are suitable for use in achieving operation ofautomated banking machines and systems in accordance with exampleembodiments.

The example embodiments of the automated banking machines and systemsdescribed herein have been described with reference to particularmethods, components and features. Other embodiments may include other ordifferent methods, components or features which provide similarfunctionality.

Thus the example embodiments achieve at least some of the above statedobjectives, eliminate difficulties encountered in the use of priordevices and systems, and attain the useful results described herein.

In the foregoing description certain terms have been described asexample embodiments for purposes of brevity, clarity and understanding.However no unnecessary limitations are to be implied therefrom becausesuch terms are used for descriptive purposes and are intended to bebroadly construed. Moreover the descriptions and illustrations hereinare by way of examples and the embodiment is not limited to the featuresshown or described.

Further, in the following claims any feature described as a means forperforming a function shall be construed as encompassing any means knownto those skilled in the art as being capable of carrying out the recitedfunction, and shall not be deemed limited to the particular means shownor described for performing the recited function in the foregoingdescription, or mere equivalents thereof.

Having described the features, discoveries and principles of theembodiments, the manner in which it is constructed and operated, any ofthe advantages and useful results attained; the new and usefulstructures, devices, elements, arrangements, parts, combinations,systems, equipment, operations, methods, processes and relationships areset forth in the appended claims.

1. Apparatus comprising: an automated banking machine, wherein theautomated banking machine includes: a reader device operative to readuser data that corresponds to a financial account, a check acceptor,wherein the check acceptor is configured to receive financial checksthat include thereon visible characters that include magnetic inkcharacter recognition (micr) symbols, wherein the check acceptorincludes:  at least one optical scanning sensor,  at least one magneticsensor,  at least one transport, wherein the at least one transport isoperative to move a check adjacent to the at least one optical scanningsensor and the at least one magnetic sensor, at least one processor, atleast one data store, wherein the at least one data store is inoperative connection with the at least one processor, wherein the atleast one data store includes processor executable software instructionsoperative to identify a plurality of visible characters included onchecks, wherein the at least one data store includes processorexecutable software instructions corresponding to at least one templatethat corresponds to at least one area on a check in which visiblecharacters may be located, wherein the at least one processor is inoperative connection with the reader device and the check acceptor,wherein the at least one processor is operative to cause a financialtransfer at least one of to and from a financial account correspondingto read user data, wherein the at least one processor is operative to:cause the at least one optical scanning sensor to scan at least one faceof a check, cause image data corresponding to visual appearance of atleast a portion of the at least one face of the check to be generated,cause modified image data to be generated, wherein the modified imagedata corresponds to an imposed coordinate system, cause at least oneportion of the image data to be selected responsive at least in part tothe at least one template and the modified image data, cause image datain the selected at least one portion to be analyzed to determine atleast one visible character within the selected at least one portion,and cause at least one message to be sent that includes datarepresentative of a determined at least one visible character.
 2. Theapparatus according to claim 1 wherein the at least one processor isoperative to determine corner image data, wherein the corner image datacorresponds to at least one corner of the check, wherein the at leastone processor is operative to generate the modified image dataresponsive at least in part to the corner image data and the imposedcoordinate system.
 3. The apparatus according to claim 2 wherein the atleast one processor is operative to determine boundary image data,wherein the boundary image data corresponds to at least two sides of thecheck, wherein the at least one processor is operative to determine thecorner image data responsive at least in part to the boundary imagedata.
 4. The apparatus according to claim 3 wherein the at least oneprocessor is operative to select a first portion of the image dataresponsive at least in part to a first template, wherein the at leastone processor is operative to determine whether the first portionincludes at least one determined character with at least a level ofassurance.
 5. The apparatus according to claim 4 wherein the at leastone processor is operative responsive at least in part to adetermination that the first portion includes the at least onedetermined character with at least the level of assurance, to cause themachine to include the data representative of the determined at leastone visible character in the at least one message sent from the machine.6. The apparatus according to claim 5 wherein the determined at leastone visible character includes at least one of a micr routing symbol anda micr transfer symbol.
 7. The apparatus according to claim 4 whereinthe at least one processor is operative responsive at least in part to adetermination that the first portion does not include at least onedetermined character with at least the level of assurance, to select asecond template, wherein the at least one processor is operativeresponsive at least in part to the second template to select a secondportion of the image data, wherein the at least one processor isoperative to determine whether the second portion includes at least onedetermined character with at least the level of assurance.
 8. Theapparatus according to claim 7 wherein the at least one processor isoperative responsive at least in part to a determination that the secondportion includes at least one determined character with at least thelevel of assurance, to cause the machine to include the datarepresentative of the determined at least one visible character in theat least one message sent from the machine.
 9. The apparatus accordingto claim 7 wherein the at least one processor is operative responsive atleast in part to a determination that the second portion does notinclude the determined at least one visible character with at least thelevel of assurance, to select a third template, wherein the at least oneprocessor is operative responsive at least in part to the third templateto select a third portion of the image data, wherein the at least oneprocessor is operative to determine whether the third portion includesthe determined at least one visible character with at least the level ofassurance.
 10. The apparatus according to claim 9, wherein the at leastone processor is operative responsive at least in part to adetermination that the third portion includes the determined at leastone visible character with at least the level of assurance, to cause themachine to include the data representative of the determined at leastone visible character in the at least one message sent from the machine.11. The apparatus according to claim 10 wherein the at least oneprocessor is operative responsive at least in part to the at least onemagnetic sensor, to produce data corresponding to a magnetic image mapcorresponding to at least an analyzed portion of the check.
 12. Theapparatus according to claim 11 wherein the at least one processor isoperative to compare magnetic image map data and the first, second, orthird image portion determined to include the determined at least onevisible character with at least the level of assurance.
 13. Theapparatus according to claim 12 wherein the at least one processor isoperative to determine whether the determined at least one visiblecharacter has magnetic properties.
 14. The apparatus according to claim13 wherein the at least one processor is operative to generate at leastone signal indicative that the check is of suspect validity responsiveat least in part to a determination that the determined at least onevisible character does not have magnetic properties.
 15. The apparatusaccording to claim 1 wherein the automated banking machine furtherincludes a cash dispenser, wherein the at least one processor is inoperative connection with the cash dispenser, wherein the at least oneprocessor is operative to cause cash to be dispensed from the machine,wherein the at least one processor is operative to cause an amountcorresponding to the dispensed cash to be transferred from the financialaccount corresponding to the read user data.
 16. The apparatus accordingto claim 1 wherein the at least one processor is operative responsive atleast in part to the at least one magnetic sensor to cause magneticimage data corresponding to magnetic properties to be generated, andwherein the at least one processor is operative responsive at least inpart to the image data and the magnetic image data to generate at leastone signal corresponding to genuineness of the check.
 17. Apparatuscomprising: an automated banking machine, wherein the automated bankingmachine includes: a reader device operative to read user data thatcorresponds to a financial account, a check acceptor, wherein the checkacceptor is configured to receive financial checks that include thereoncharacters that include magnetic ink character recognition (micr)symbols, wherein the check acceptor includes:  at least one opticalscanning sensor,  at least one magnetic sensor,  at least one transport,wherein the at least one transport is operative to move a check adjacentto the at least one optical scanning sensor and the at least onemagnetic sensor, at least one processor, at least one data store,wherein the at least one data store is in operative connection with theat least one processor, wherein the at least one data store includesprocessor executable software instructions operative to identify aplurality of characters included on checks, wherein the at least onedata store includes processor executable software instructionscorresponding to at least one template that corresponds to at least onearea on a check in which characters may be located, wherein the at leastone processor is in operative connection with the reader device and thecheck acceptor, wherein the at least one processor inoperative to causea financial transfer at least one of to and from a financial accountcorresponding to read user data, wherein the at least one processor isoperative to: cause the at least one optical scanning sensor to scan atleast one face of a check, cause image data corresponding to visualappearance of at least a portion of the at least one face of the checkto be generated, wherein the at least one processor is operative toselect a first template, wherein the at least one processor is operativeto select a first portion of the image data responsive at least in partto the first template, wherein the at least one processor is operativeto determine whether the first portion includes the at least onecharacter with at least a level of assurance, wherein the at least oneprocessor is operative responsive at least in part to a determinationthat the first portion includes at least one character with at least thelevel of assurance, to cause the machine to include data representativeof the at least one character, in at least one message sent from themachine, wherein the at least one processor is operative responsive atleast in part to a determination that the first portion does not includeat least one character with at least the level of assurance, to select asecond template different from the first template,  wherein the at leastone processor is operative responsive at least in part to the secondtemplate to select a second portion of the image data different from thefirst portion,  wherein the at least one processor is operative todetermine whether the second portion includes at least one characterwith at least the level of assurance.
 18. The apparatus according toclaim 17 wherein the at least one processor is operative responsive atleast in part to the at least one magnetic sensor to further determineif the determined at least one character has magnetic properties, andwherein the at least one processor is operative responsive at least inpart to the further determination to generate at least one signalindicative of genuineness of the check.